Autonomous Driving

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Autonomous driving studies how vehicles can sense their surroundings, anticipate other road users, and choose safe, effective actions in traffic. Systems range from modular stacks that connect camera, LiDAR, and radar perception to prediction and model-predictive control, to end-to-end policies trained with imitation learning, reinforcement learning, or vision-language and world models. Researchers evaluate these methods on datasets such as nuScenes, Waymo, and Argoverse, in simulators such as CARLA and nuPlan, and through physical vehicle tests. A central challenge is turning strong benchmark results into reliable closed-loop behavior across rare events, changing conditions, and real-world deployment constraints.

Latest papers

538 papers53 papers in the last four weeks, down 9% on the four weeks before. 0.3% of all new papers.

Apr 12, 2026

BridgeSim: Unveiling the OL-CL Gap in End-to-End Autonomous Driving

Open-loop (OL) to closed-loop (CL) gap (OL-CL gap) exists when OL-pretrained policies scoring high in OL evaluations fail to transfer effectively in closed-loop (CL) deployment. In this paper, we unveil the root causes of this systemic failure and propose a practical remedy. Specifically, we demonstrate that OL policies suffer from Observational Domain Shift and Objective Mismatch. We show that while the former is largely recoverable with adaptation techniques, the latter creates a structural inability to model complex reactive behaviors, which forms the primary OL-CL gap. We find that a wide range of OL policies learn a biased Q-value estimator that neglects both the reactive nature of CL simulations and the temporal awareness needed to reduce compounding errors. To this end, we propose a Test-Time Adaptation (TTA) framework that calibrates observational shift, reduces state-action biases, and enforces temporal consistency. Extensive experiments show that TTA effectively mitigates planning biases and yields superior scaling dynamics than its baseline counterparts. Furthermore, our analysis highlights the existence of blind spots in standard OL evaluation protocols that fail to capture the realities of closed-loop deployment.

Seth Z. Zhao, Luobin Wang, Hongwei Ruan +13

UCLA · UCSD · Qualcomm

End-to-End Autonomous Driving Autonomous Driving Autonomous Driving Planning Test-Time Adaptation

Apr 11, 2026

SEIDM: A Safe and Efficient Intelligent Driver Model for Autonomous Driving Behavior

The Intelligent Driver Model (IDM) is a cornerstone of Adaptive Cruise Control (ACC), valued for its interpretable parameters and effectiveness in car-following behavior modeling. However, its inherent conservatism leads to prolonged stabilization and reduced traffic efficiency, which have received limited attention. In this paper, we propose SEIDM (Safe and Efficient Intelligent Driver Model), an enhanced IDM extension designed to improve traffic flow efficiency without sacrificing safety. SEIDM introduces an adaptive safety factor to dynamically modulate the impact of the safe deceleration term in acceleration decisions. This allows vehicles to follow more assertively under safe conditions while behaving more cautiously in potential hazards. Extensive urban traffic simulations show that SEIDM achieves significantly shorter stabilization spacing and faster convergence to traffic flow equilibrium, outperforming the original IDM and its variants in traffic stability and efficiency.

Yuyang Yao, Shaocheng Luo

Department of Information and Communication Engineering, Tongji University, Shanghai, China · Department of Electrical and Computer Engineering, Duke University, Durham, NC, USA

Autonomous Driving Intelligent Transportation Systems

Apr 10, 2026

Decentralized Opinion-Integrated Decision making at Unsignalized Intersections via Signed Networks

In this letter, we consider the problem of decentralized decision making among connected autonomous vehicles at unsignalized intersections, where existing centralized approaches do not scale gracefully under mixed maneuver intentions and coordinator failure. We propose a closed-loop opinion-dynamic decision model for intersection coordination, where vehicles exchange intent through dual signed networks: a conflict topology based communication network and a commitment-driven belief network that enable cooperation without a centralized coordinator. Continuous opinion states modulate velocity optimizer weights prior to commitment; a closed-form predictive feasibility gate then freezes each vehicle's decision into a GO or YIELD commitment, which propagates back through the belief network to pre-condition neighbor behavior ahead of physical conflicts. Crossing order emerges from geometric feasibility and arrival priority without the use of joint optimization or a solver. The approach is validated across three scenarios spanning fully competitive, merge, and mixed conflict topologies. The results demonstrate collision-free coordination and lower last-vehicle exit times compared to first come first served (FCFS) in all conflict non-trivial configurations.

Bhaskar Varma, Ying Shuai Quan, Karl D. von Ellenrieder +1

Faculty of Engineering, Free University of Bozen-Bolzano, Italy · Mechatronics Group, Department of Electrical Engineering, Chalmers University of Technology, Gothenburg, Sweden

Multi-Agent Coordination Intelligent Transportation Systems Autonomous Driving Opinion Dynamics

Apr 10, 2026

VAGNet: Vision-based Accident Anticipation with Global Features

Traffic accidents are a leading cause of fatalities and injuries across the globe. Therefore, the ability to anticipate hazardous situations in advance is essential. Automated accident anticipation enables timely intervention through driver alerts and collision avoidance maneuvers, forming a key component of advanced driver assistance systems. In autonomous driving, such predictive capabilities support proactive safety behaviors, such as initiating defensive driving and human takeover when required. Using dash-cam video as input offers a cost-effective solution, but it is challenging due to the complexity of real-world driving scenes. Accident anticipation systems need to operate in real-time. However, current methods involve extracting features from each detected object, which is computationally intensive. We propose VAGNet, a deep neural network that learns to predict accidents from dash-cam video using global features of traffic scenes without requiring explicit object-level features. The network consists of transformer and graph modules, and we use the vision foundation model VideoMAE-V2 for global feature extraction. Experiments on four benchmark datasets (DAD, DoTA, DADA, and Nexar) show that our method anticipates accidents with higher average precision while being computationally more efficient compared to existing methods. It also achieves higher mean time-to-accident scores in most cases, while being competitive in others.

Vipooshan Vipulananthan, Charith D. Chitraranjan

Department of Computer Science and Engineering, University of Moratuwa, Katubedda 10400, Sri Lanka

Traffic Accident Anticipation Autonomous Driving

Apr 9, 2026

LMGenDrive: Bridging Multimodal Understanding and Generative World Modeling for End-to-End Driving

Recent years have seen remarkable progress in autonomous driving, yet generalization to long-tail and open-world scenarios remains a major bottleneck for large-scale deployment. To address this challenge, some works use LLMs and VLMs for vision-language understanding and reasoning, enabling vehicles to interpret rare and safety-critical situations when generating actions. Others study generative world models to capture the spatio-temporal evolution of driving scenes, allowing agents to imagine possible futures before acting. Inspired by human intelligence, which unifies understanding and imagination, we explore a unified model for autonomous driving. We present LMGenDrive, the first framework that combines LLM-based multimodal understanding with generative world models for end-to-end closed-loop driving. Given multi-view camera inputs and natural-language instructions, LMGenDrive generates both future driving videos and control signals. This design provides complementary benefits: video prediction improves spatio-temporal scene modeling, while the LLM contributes strong semantic priors and instruction grounding from large-scale pretraining. We further propose a progressive three-stage training strategy, from vision pretraining to multi-step long-horizon driving, to improve stability and performance. LMGenDrive supports both low-latency online planning and autoregressive offline video generation. Experiments show that it significantly outperforms prior methods on challenging closed-loop benchmarks, with clear gains in instruction following, spatio-temporal understanding, and robustness to rare scenarios. These results suggest that unifying multimodal understanding and generation is a promising direction for more generalizable and robust embodied decision-making systems.

Hao Shao, Letian Wang, Yang Zhou +5

CUHK MMLab · University of Toronto · UC Berkeley

Autonomous Driving End-to-End Autonomous Driving World Models for Autonomous Driving VLMs for Autonomous Driving

Apr 9, 2026

Fail2Drive: Benchmarking Closed-Loop Driving Generalization

Generalization under distribution shift remains a central bottleneck for closed-loop autonomous driving. Although simulators like CARLA enable safe and scalable testing, existing benchmarks rarely measure true generalization: they typically reuse training scenarios at test time. Success can therefore reflect memorization rather than robust driving behavior. We introduce Fail2Drive, the first paired-route benchmark for closed-loop generalization in CARLA, with 200 routes and 17 new scenario classes spanning appearance, layout, behavioral, and robustness shifts. Each shifted route is matched with an in-distribution counterpart, isolating the effect of the shift and turning qualitative failures into quantitative diagnostics. Evaluating multiple state-of-the-art models reveals consistent degradation, with an average success-rate drop of 22.8%. Our analysis uncovers unexpected failure modes, such as ignoring objects clearly visible in the LiDAR and failing to learn the fundamental concepts of free and occupied space. To accelerate follow-up work, Fail2Drive includes an open-source toolbox for creating new scenarios and validating solvability via a privileged expert policy. Together, these components establish a reproducible foundation for benchmarking and improving closed-loop driving generalization. We open-source all code, data, and tools at https://github.com/autonomousvision/fail2drive .

Simon Gerstenecker, Andreas Geiger, Katrin Renz

University of Tübingen Tübingen AI Center

Autonomous Driving Benchmarks Autonomous Driving Autonomous Driving Simulation Autonomous Driving Scenario Generation

Apr 9, 2026

CrashSight: A Phase-Aware, Infrastructure-Centric Video Benchmark for Traffic Crash Scene Understanding and Reasoning

Cooperative autonomous driving requires traffic scene understanding from both vehicle and infrastructure perspectives. While vision-language models (VLMs) show strong general reasoning capabilities, their performance in safety-critical traffic scenarios remains insufficiently evaluated due to the ego-vehicle focus of existing benchmarks. To bridge this gap, we present CrashSight, a large-scale vision-language benchmark for roadway crash understanding using real-world roadside camera data. The dataset comprises 250 crash videos, annotated with 13K multiple-choice question-answer pairs organized under a two-tier taxonomy. Tier 1 evaluates the visual grounding of scene context and involved parties, while Tier 2 probes higher-level reasoning, including crash mechanics, causal attribution, temporal progression, and post-crash outcomes. We benchmark 8 state-of-the-art VLMs and show that, despite strong scene description capabilities, current models struggle with temporal and causal reasoning in safety-critical scenarios. We provide a detailed analysis of failure scenarios and discuss directions for improving VLM crash understanding. The benchmark provides a standardized evaluation framework for infrastructure-assisted perception in cooperative autonomous driving. The CrashSight benchmark, including the full dataset and code, is accessible at https://mcgrche.github.io/crashsight.

Rui Gan, Junyi Ma, Pei Li +4

University of Wisconsin–Madison · University of Wyoming · Columbia University

Autonomous Driving Benchmarks Autonomous Driving Safety Evaluation Autonomous Driving Temporal Video Understanding

Apr 9, 2026

Open-Ended Instruction Realization with LLM-Enabled Multi-Planner Scheduling in Autonomous Vehicles

Most Human-Machine Interaction (HMI) research overlooks the maneuvering needs of passengers in autonomous driving (AD). Natural language offers an intuitive interface, yet translating passenger open-ended instructions into control signals, without sacrificing interpretability and traceability, remains a challenge. This study proposes an instruction-realization framework that leverages a large language model (LLM) to interpret instructions, generates executable scripts that schedule multiple model predictive control (MPC)-based motion planners based on real-time feedback, and converts planned trajectories into control signals. This scheduling-centric design decouples semantic reasoning from vehicle control at different timescales, establishing a transparent, traceable decision-making chain from high-level instructions to low-level actions. Due to the absence of high-fidelity evaluation tools, this study introduces a benchmark for open-ended instruction realization in a closed-loop setting. Comprehensive experiments reveal that the framework significantly improves task-completion rates over instruction-realization baselines, reduces LLM query costs, achieves safety and compliance on par with specialized AD approaches, and exhibits considerable tolerance to LLM inference latency. For more qualitative illustrations and a clearer understanding.

Jiawei Liu, Xun Gong, Fen Fang +6

Jilin University, China · Engineering Research Center of Knowledge-Driven Human-Machine Intelligence, MOE, China · Agency for Science, Technology and Research (A*STAR), Singapore +3

Autonomous Driving Autonomous Driving Planning Language Model-Based Control Autonomous Driving Benchmarks

Apr 8, 2026

Geo-EVS: Geometry-Conditioned Extrapolative View Synthesis for Autonomous Driving

Extrapolative novel view synthesis can reduce camera-rig dependency in autonomous driving by generating standardized virtual views from heterogeneous sensors. Existing methods degrade outside recorded trajectories because extrapolated poses provide weak geometric support and no dense target-view supervision. The key is to explicitly expose the model to out-of-trajectory condition defects during training. We propose Geo-EVS, a geometry-conditioned framework under sparse supervision. Geo-EVS has two components. Geometry-Aware Reprojection (GAR) uses fine-tuned VGGT to reconstruct colored point clouds and reproject them to observed and virtual target poses, producing geometric condition maps. This design unifies the reprojection path between training and inference. Artifact-Guided Latent Diffusion (AGLD) injects reprojection-derived artifact masks during training so the model learns to recover structure under missing support. For evaluation, we use a LiDAR-Projected Sparse-Reference (LPSR) protocol when dense extrapolated-view ground truth is unavailable. On Waymo, Geo-EVS improves sparse-view synthesis quality and geometric accuracy, especially in high-angle and low-coverage settings. It also improves downstream 3D detection.

Yatong Lan, Rongkui Tang, Lei He

School of Vehicle and Mobility, Tsinghua University, Beijing, China · State Key Laboratory of Intelligent Green Vehicle and Mobility, Tsinghua University, Beijing, China · School of Science, Minzu University, Beijing, China +1

Novel View Synthesis Autonomous Driving Latent Diffusion Models

Apr 6, 2026

Adversarial Robustness Analysis of Cloud-Assisted Autonomous Driving Systems

Autonomous vehicles increasingly rely on deep learning-based perception and control, which impose substantial computational demands. Cloud-assisted architectures offload these functions to remote servers, enabling enhanced perception and coordinated decision-making through the Internet of Vehicles (IoV). However, this paradigm introduces cross-layer vulnerabilities, where adversarial manipulation of perception models and network impairments in the vehicle-cloud link can jointly undermine safety-critical autonomy. This paper presents a hardware-in-the-loop IoV testbed that integrates real-time perception, control, and communication to evaluate such vulnerabilities in cloud-assisted autonomous driving. A YOLOv8-based object detector deployed on the cloud is subjected to whitebox adversarial attacks using the Fast Gradient Sign Method (FGSM) and Projected Gradient Descent (PGD), while network adversaries induce delay and packet loss in the vehicle-cloud loop. Results show that adversarial perturbations significantly degrade perception performance, with PGD reducing detection precision and recall from 0.73 and 0.68 in the clean baseline to 0.22 and 0.15 at epsilon= 0.04. Network delays of 150-250 ms, corresponding to transient losses of approximately 3-4 frames, and packet loss rates of 0.5-5 % further destabilize closed-loop control, leading to delayed actuation and rule violations. These findings highlight the need for cross-layer resilience in cloud-assisted autonomous driving systems.

Maher Al Islam, Amr S. El-Wakeel

Lane Department of Computer Science and Electrical Engineering, West Virginia University, WV, USA

Adversarial Attacks on Object Detectors Adversarial Robustness Autonomous Driving Autonomous Driving Safety Evaluation Adversarial Attacks Autonomous Driving Perception

Apr 5, 2026

DriveVA: Video Action Models are Zero-Shot Drivers

Generalization is a central challenge in autonomous driving, as real-world deployment requires robust performance under unseen scenarios, sensor domains, and environmental conditions. Recent world-model-based planning methods have shown strong capabilities in scene understanding and multi-modal future prediction, yet their generalization across datasets and sensor configurations remains limited. In addition, their loosely coupled planning paradigm often leads to poor video-trajectory consistency during visual imagination. To overcome these limitations, we propose DriveVA, a novel autonomous driving world model that jointly decodes future visual forecasts and action sequences in a shared latent generative process. DriveVA inherits rich priors on motion dynamics and physical plausibility from well-pretrained large-scale video generation models to capture continuous spatiotemporal evolution and causal interaction patterns. To this end, DriveVA employs a DiT-based decoder to jointly predict future action sequences (trajectories) and videos, enabling tighter alignment between planning and scene evolution. We also introduce a video continuation strategy to strengthen long-duration rollout consistency. DriveVA achieves an impressive PDM-based planning performance of 90.9 PDM score on the NAVSIM benchmark. Extensive experiments also demonstrate the zero-shot capability and cross-domain generalization of DriveVA, which reduces average L2 error and collision rate by 78.9% and 83.3% on nuScenes and 52.5% and 52.4% on the Bench2Drive built on CARLA v2 compared with the state-of-the-art world-model-based planner.

Mengmeng Liu, Diankun Zhang, Jiuming Liu +7

University of Twente, The Netherlands · Xiaomi EV, China · University of Cambridge, United Kingdom +1

Action-Conditioned Video Generation World Models Autonomous Driving World Models for Autonomous Driving Autonomous Driving Planning

Apr 3, 2026

V2X-QA: A Comprehensive Reasoning Dataset and Benchmark for Multimodal Large Language Models in Autonomous Driving Across Ego, Infrastructure, and Cooperative Views

Multimodal large language models (MLLMs) have shown strong potential for autonomous driving, yet existing benchmarks remain largely ego-centric and therefore cannot systematically assess model performance in infrastructure-centric and cooperative driving conditions. In this work, we introduce V2X-QA, a real-world dataset and benchmark for evaluating MLLMs across vehicle-side, infrastructure-side, and cooperative viewpoints. V2X-QA is built around a view-decoupled evaluation protocol that enables controlled comparison under vehicle-only, infrastructure-only, and cooperative driving conditions within a unified multiple-choice question answering (MCQA) framework. The benchmark is organized into a twelve-task taxonomy spanning perception, prediction, and reasoning and planning, and is constructed through expert-verified MCQA annotation to enable fine-grained diagnosis of viewpoint-dependent capabilities. Benchmark results across ten representative state-of-the-art proprietary and open-source models show that viewpoint accessibility substantially affects performance, and infrastructure-side reasoning supports meaningful macroscopic traffic understanding. Results also indicate that cooperative reasoning remains challenging since it requires cross-view alignment and evidence integration rather than simply additional visual input. To address these challenges, we introduce V2X-MoE, a benchmark-aligned baseline with explicit view routing and viewpoint-specific LoRA experts. The strong performance of V2X-MoE further suggests that explicit viewpoint specialization is a promising direction for multi-view reasoning in autonomous driving. Overall, V2X-QA provides a foundation for studying multi-perspective reasoning, reliability, and cooperative physical intelligence in connected autonomous driving. The dataset and V2X-MoE resources are publicly available at: https://github.com/junwei0001/V2X-QA.

Junwei You, Pei Li, Zhuoyu Jiang +7

Department of Civil and Environmental Engineering, University of Wisconsin–Madison, Madison, WI, 53706, USA · Department of Civil and Architectural Engineering and Construction Management, University of Wyoming, Laramie, WY, 82071, USA · Kuaishou Technology, Beijing, 100085, China +1

Autonomous Driving Benchmarks VLMs for Autonomous Driving Autonomous Driving Datasets Autonomous Driving Multimodal Large Language Models

Apr 3, 2026

Cross-Vehicle 3D Geometric Consistency for Self-Supervised Surround Depth Estimation on Articulated Vehicles

Surround depth estimation provides a cost-effective alternative to LiDAR for 3D perception in autonomous driving. While recent self-supervised methods explore multi-camera settings to improve scale awareness and scene coverage, they are primarily designed for passenger vehicles and rarely consider articulated vehicles or robotics platforms. The articulated structure introduces complex cross-segment geometry and motion coupling, making consistent depth reasoning across views more challenging. In this work, we propose ArticuSurDepth, a self-supervised framework for surround-view depth estimation on articulated vehicles that enhances depth learning through cross-view and cross-vehicle geometric consistency guided by structural priors from vision foundation model. Specifically, we introduce multi-view spatial context enrichment strategy and a cross-view surface normal constraint to improve structural coherence across spatial and temporal contexts. We further incorporate camera height regularization with ground plane-awareness to encourage metric depth estimation, together with cross-vehicle pose consistency that bridges motion estimation between articulated segments. To validate our proposed method, an articulated vehicle experiment platform was established with a dataset collected over it. Experiment results demonstrate state-of-the-art (SoTA) performance of depth estimation on our self-collected dataset as well as on DDAD, nuScenes, and KITTI benchmarks.

Weimin Liu, Jiyuan Qiu, Wenjun Wang +1

School of vehicle and Mobility, Tsinghua University, Beijing, China · Remote Sensing and Earth Observation Laboratory, University of Copenhagen, Copenhagen K, Denmark · California PATH, University of California, Berkeley, CA, USA

Self-Supervised Monocular Depth Estimation Depth Estimation Multi-View Depth Estimation Autonomous Driving Multi-View Geometry

Apr 2, 2026

Adaptive Learned State Estimation based on KalmanNet

Hybrid state estimators that combine model-based Kalman filtering with learned components have shown promise on simulated data, yet their performance on real-world automotive data remains insufficient. In this work we present Adaptive Multi-modal KalmanNet (AM-KNet), an advancement of KalmanNet tailored to the multi-sensor autonomous driving setting. AM-KNet introduces sensor-specific measurement modules that enable the network to learn the distinct noise characteristics of radar, lidar, and camera independently. A hypernetwork with context modulation conditions the filter on target type, motion state, and relative pose, allowing adaptation to diverse traffic scenarios. We further incorporate a covariance estimation branch based on the Josephs form and supervise it through negative log-likelihood losses on both the estimation error and the innovation. A comprehensive, component-wise loss function encodes physical priors on sensor reliability, target class, motion state, and measurement flow consistency. AM-KNet is trained and evaluated on the nuScenes and View-of-Delft datasets. The results demonstrate improved estimation accuracy and tracking stability compared to the base KalmanNet, narrowing the performance gap with classical Bayesian filters on real-world automotive data.

Arian Mehrfard, Bharanidhar Duraisamy, Stefan Haag +2

Research and Development, Mercedes-Benz AG, Germany · Institute for Autonomous Driving, University of the Bundeswehr Munich, Germany

Robot State Estimation Kalman Filtering Multimodal Sensor Fusion Autonomous Driving

Apr 2, 2026

LEO: Graph Attention Network based Hybrid Multi Sensor Extended Object Fusion and Tracking for Autonomous Driving Applications

Accurate shape and trajectory estimation of dynamic objects is essential for reliable automated driving. Classical Bayesian extended-object models offer theoretical robustness and efficiency but depend on completeness of a-priori and update-likelihood functions, while deep learning methods bring adaptability at the cost of dense annotations and high compute. We bridge these strengths with LEO (Learned Extension of Objects), a spatio-temporal Graph Attention Network that fuses multi-modal production-grade sensor tracks to learn adaptive fusion weights, ensure temporal consistency, and represent multi-scale shapes. Using a task-specific parallelogram ground-truth formulation, LEO models complex geometries (e.g. articulated trucks and trailers) and generalizes across sensor types, configurations, object classes, and regions, remaining robust for challenging and long-range targets. Evaluations on the Mercedes-Benz DRIVE PILOT SAE L3 dataset demonstrate real-time computational efficiency suitable for production systems; additional validation on public datasets such as View of Delft (VoD) further confirms cross-dataset generalization.

Mayank Mayank, Bharanidhar Duraisamy, Florian Geiss

Research and Development, Mercedes-Benz AG, Germany

Multi-Object Tracking Autonomous Driving Multimodal Sensor Fusion 3D MOT

Apr 1, 2026

Efficient Equivariant Transformer for Self-Driving Agent Modeling

Accurately modeling agent behaviors is an important task in self-driving. It is also a task with many symmetries, such as equivariance to the order of agents and objects in the scene or equivariance to arbitrary roto-translations of the entire scene as a whole; i.e., SE(2)-equivariance. The transformer architecture is a ubiquitous tool for modeling these symmetries. While standard self-attention is inherently permutation equivariant, explicit pairwise relative positional encodings have been the standard for introducing SE(2)-equivariance. However, this approach introduces an additional cost that is quadratic in the number of agents, limiting its scalability to larger scenes and batch sizes. In this work, we propose DriveGATr, a novel transformer-based architecture for agent modeling that achieves SE(2)-equivariance without the computational cost of existing methods. Inspired by recent advances in geometric deep learning, DriveGATr encodes scene elements as multivectors in the 2D projective geometric algebra

\mathbb{R}^*_{2,0,1}

and processes them with a stack of equivariant transformer blocks. Crucially, DriveGATr models geometric relationships using standard attention between multivectors, eliminating the need for costly explicit pairwise relative positional encodings. Experiments on the Waymo Open Motion Dataset demonstrate that DriveGATr is comparable to the state-of-the-art in traffic simulation and establishes a superior Pareto front for performance vs computational cost.

Scott Xu, Dian Chen, Kelvin Wong +3

Waabi · University of Toronto

Autonomous Driving Equivariant Neural Networks Autonomous Driving Simulation Autonomous Driving Perception

Apr 1, 2026

VRUD: A Drone Dataset for Complex Vehicle-VRU Interactions within Mixed Traffic

The Operational Design Domain (ODD) of urbanoriented Level 4 (L4) autonomous driving, especially for autonomous robotaxis, confronts formidable challenges in complex urban mixed traffic environments. These challenges stem mainly from the high density of Vulnerable Road Users (VRUs) and their highly uncertain and unpredictable interaction behaviors. However, existing open-source datasets predominantly focus on structured scenarios such as highways or regulated intersections, leaving a critical gap in data representing chaotic, unstructured urban environments. To address this, this paper proposes an efficient, high-precision method for constructing drone-based datasets and establishes the Vehicle-Vulnerable Road User Interaction Dataset (VRUD), as illustrated in Figure 1. Distinct from prior works, VRUD is collected from typical "Urban Villages" in Shenzhen, characterized by loose traffic supervision and extreme occlusion. The dataset comprises 4 hours of 4K/30Hz recording, containing 11,479 VRU trajectories and 1,939 vehicle trajectories. A key characteristic of VRUD is its composition: VRUs account for about 87% of all traffic participants, significantly exceeding the proportions in existing benchmarks. Furthermore, unlike datasets that only provide raw trajectories, we extracted 4,002 multi-agent interaction scenarios based on a novel Vector Time to Collision (VTTC) threshold, supported by standard OpenDRIVE HD maps. This study provides valuable, rare edge-case resources for enhancing the safety performance of ADS in complex, unstructured urban environments. To facilitate further research, we have made the VRUD dataset open-source at: https://zzi4.github.io/VRUD/.

Ziyu Wang, Hongrui Kou, Cheng Wang +4

National Key Laboratory of Automotive Chassis Integration and Bionics, Jilin University, Changchun 130025, China · DRIVEResearch, Changchun, China · School of Engineering and Physical Sciences, Heriot-Watt University, Edinburgh EH1 4AS, U.K. +1

Autonomous Driving Datasets Autonomous Driving Robotic Data Collection

Apr 1, 2026

LLM-Generated Fault Scenarios for Evaluating Perception-Driven Lane Following in Autonomous Edge Systems

Deploying autonomous vision systems on edge devices faces a critical challenge: resource constraints prevent real-time and predictable execution of comprehensive safety tests. Existing validation methods depend on static datasets or manual fault injection, failing to capture the diverse environmental hazards encountered in real-world deployment. To address this, we introduce a decoupled offline-online fault injection framework. This architecture separates the validation process into two distinct phases: a computationally intensive Offline Phase and a lightweight Online Phase. In the offline phase, we employ Large Language Models (LLMs) to semantically generate structured fault scenarios and Latent Diffusion Models (LDMs) to synthesize high-fidelity sensor degradations. These complex fault dynamics are distilled into a pre-computed lookup table, enabling the edge device to perform real-time fault-aware inference without running heavy AI models locally. We extensively validated this framework on a ResNet18 lane-following model across 460 fault scenarios. Results show that while the model achieves a baseline R^2 of approximately 0.85 on clean data, our generated faults expose significant robustness degradation, with RMSE increasing by up to 99% and within-0.10 localization accuracy dropping to as low as 31.0% under fog conditions, demonstrating the inadequacy of normal-data evaluation for real-world edge AI deployment.

Faezeh Pasandideh, Achim Rettberg

Department of Electrical Engineering Hamm-Lippstadt University. of Applied Sciences (HSHL) Lippstadt, Germany

Autonomous Driving Safety Evaluation Autonomous Driving Autonomous Driving Scenario Generation Edge Inference

Apr 1, 2026

COTTA: Context-Aware Transfer Adaptation for Trajectory Prediction in Autonomous Driving

Developing robust models to accurately predict the trajectories of surrounding agents is fundamental to autonomous driving safety. However, most public datasets, such as the Waymo Open Motion Dataset and Argoverse, are collected in Western road environments and do not reflect the unique traffic patterns, infrastructure, and driving behaviors of other regions, including South Korea. This domain discrepancy leads to performance degradation when state-of-the-art models trained on Western data are deployed in different geographic contexts. In this work, we investigate the adaptability of Query-Centric Trajectory Prediction (QCNet) when transferred from U.S.-based data to Korean road environments. Using a Korean autonomous driving dataset, we compare four training strategies: zero-shot transfer, training from scratch, full fine-tuning, and encoder freezing. Experimental results demonstrate that leveraging pretrained knowledge significantly improves prediction performance. Specifically, selectively fine-tuning the decoder while freezing the encoder yields the best trade-off between accuracy and training efficiency, reducing prediction error by over 66% compared to training from scratch. This study provides practical insights into effective transfer learning strategies for deploying trajectory prediction models in new geographic domains.

Seohyoung Park, Jaeyeol Lim, Seoyoung Ju +3

Ewha Womans University · OUTTA · Seoul National University +2

Trajectory Prediction Autonomous Driving Multi-Agent Trajectory Prediction Cross-Domain Transfer Learning

Mar 31, 2026

C-TRAIL: A Commonsense World Framework for Trajectory Planning in Autonomous Driving

Trajectory planning for autonomous driving increasingly leverages large language models (LLMs) for commonsense reasoning, yet LLM outputs are inherently unreliable, posing risks in safety-critical applications. We propose C-TRAIL, a framework built on a Commonsense World that couples LLM-derived commonsense with a trust mechanism to guide trajectory planning. C-TRAIL operates through a closed-loop Recall, Plan, and Update cycle: the Recall module queries an LLM for semantic relations and quantifies their reliability via a dual-trust mechanism; the Plan module injects trust-weighted commonsense into Monte Carlo Tree Search (MCTS) through a Dirichlet trust policy; and the Update module adaptively refines trust scores and policy parameters from environmental feedback. Experiments on four simulated scenarios in Highway-env and two real-world levelXData datasets (highD, rounD) show that C-TRAIL consistently outperforms state-of-the-art baselines, reducing ADE by 40.2%, FDE by 51.7%, and improving SR by 16.9 percentage points on average. The source code is available at https://github.com/ZhihongCui/CTRAIL.

Zhihong Cui, Haoran Tang, Tianyi Li +4

the Department of Informatics, University of Oslo, Norway · the Department of Computing, Hong Kong Polytechnic University, Hong Kong, China · the Department of Computer Science, Aalborg University, Denmark

Autonomous Driving Planning Autonomous Driving

Mar 30, 2026

Energy-Aware Imitation Learning for Steering Prediction Using Events and Frames

In autonomous driving, relying solely on frame-based cameras can lead to inaccuracies caused by factors like long exposure times, high-speed motion, and challenging lighting conditions. To address these issues, we introduce a bio-inspired vision sensor known as the event camera. Unlike conventional cameras, event cameras capture sparse, asynchronous events that provide a complementary modality to mitigate these challenges. In this work, we propose an energy-aware imitation learning framework for steering prediction that leverages both events and frames. Specifically, we design an Energy-driven Cross-modality Fusion Module (ECFM) and an energy-aware decoder to produce reliable and safe predictions. Extensive experiments on two public real-world datasets, DDD20 and DRFuser, demonstrate that our method outperforms existing state-of-the-art (SOTA) approaches. The codes and trained models will be released upon acceptance.

Hu Cao, Jiong Liu, Xingzhuo Yan +5

School of Automation, Southeast University, Sipailou 2, Nanjing, 210096, China. · Chair of Robotics, Artificial Intelligence and Real-time Systems, Technical University of Munich, Boltzmannstraße 15, Munich, 85748, Germany. · School of Computer Science and Technology, Tongji University, Cao’an Road, Shanghai, 200070, China.

Autonomous Driving Imitation Learning Multimodal Sensor Fusion Event-Based Vision

Mar 29, 2026

Dual-Stage LLM Framework for Scenario-Centric Semantic Interpretation in Driving Assistance

Advanced Driver Assistance Systems (ADAS) increasingly rely on learning-based perception, yet safety-relevant failures often arise without component malfunction, driven instead by partial observability and semantic ambiguity in how risk is interpreted and communicated. This paper presents a scenario-centric framework for reproducible auditing of LLM-based risk reasoning in urban driving contexts. Deterministic, temporally bounded scenario windows are constructed from multimodal driving data and evaluated under fixed prompt constraints and a closed numeric risk schema, ensuring structured and comparable outputs across models. Experiments on a curated near-people scenario set compare two text-only models and one multimodal model under identical inputs and prompts. Results reveal systematic inter-model divergence in severity assignment, high-risk escalation, evidence use, and causal attribution. Disagreement extends to the interpretation of vulnerable road user presence, indicating that variability often reflects intrinsic semantic indeterminacy rather than isolated model failure. These findings highlight the importance of scenario-centric auditing and explicit ambiguity management when integrating LLM-based reasoning into safety-aligned driver assistance systems.

Jean Douglas Carvalho, Hugo Taciro Kenji, Ahmad Mohammad Saber +3

LLM Auditing Autonomous Driving Safety Evaluation Autonomous Driving

Mar 28, 2026

Robust Global-Local Behavior Arbitration via Continuous Command Fusion Under LiDAR Errors

Modular autonomous driving systems must coordinate global progress objectives with local safety-driven reactions under imperfect sensing and strict real-time constraints. This paper presents a ROS2-native arbitration module that continuously fuses the outputs of two unchanged and interpretable controllers: a global reference-tracking controller based on Pure Pursuit and a reactive LiDAR-based Gap Follow controller. At each control step, both controllers propose Ackermann commands, and a PPO-trained policy predicts a continuous gate from a compact feature observation to produce a single fused drive command, augmented with practical safety checks. For comparison under identical ROS topic inputs and control rate, we implement a lightweight sampling-based predictive baseline. Robustness is evaluated using a ROS2 impairment protocol that injects LiDAR noise, delay, and dropout, and additionally sweeps forward-cone false short-range outliers. In a repeatable close-proximity passing scenario, we report safe success and failure rates together with per-step end-to-end controller runtime as sensing stress increases. The study is intended as a command-level robustness evaluation in a modular ROS2 setting, not as a replacement for planning-level interaction reasoning.

Mohamed Elgouhary, Amr S. El-Wakeel

The Authors are with the Lane Department of Computer Science and Electrical Engineering, West Virginia University, Morgantown, WV, USA.

Autonomous Driving Autonomous Driving Safety Evaluation RL for Autonomous Driving

Mar 28, 2026

Autonomous overtaking trajectory optimization using reinforcement learning and opponent pose estimation

Vehicle overtaking is one of the most complex driving maneuvers for autonomous vehicles. To achieve optimal autonomous overtaking, driving systems rely on multiple sensors that enable safe trajectory optimization and overtaking efficiency. This paper presents a reinforcement learning mechanism for multi-agent autonomous racing environments, enabling overtaking trajectory optimization, based on LiDAR and depth image data. The developed reinforcement learning agent uses pre-generated raceline data and sensor inputs to compute the steering angle and linear velocity for optimal overtaking. The system uses LiDAR with a 2D detection algorithm and a depth camera with YOLO-based object detection to identify the vehicle to be overtaken and its pose. The LiDAR and the depth camera detection data are fused using a UKF for improved opponent pose estimation and trajectory optimization for overtaking in racing scenarios. The results show that the proposed algorithm successfully performs overtaking maneuvers in both simulation and real-world experiments, with pose estimation RMSE of (0.0816, 0.0531) m in (x, y).

Matej Rene Cihlar, Luka Šiktar, Branimir Ćaran +1

University of Zagreb, Zagreb 10000, Croatia, Faculty of mechanical engineering and naval architecture

Autonomous Driving Autonomous Racing RL for Autonomous Driving Autonomous Driving Perception

Mar 27, 2026

R-PGA: Robust Physical Adversarial Camouflage Generation via Relightable 3D Gaussian Splatting

Physical adversarial camouflage poses a severe security threat to autonomous driving systems by mapping adversarial textures onto 3D objects. Nevertheless, current methods remain brittle in complex dynamic scenarios, failing to generalize across diverse geometric (e.g., viewing configurations) and radiometric (e.g., dynamic illumination, atmospheric scattering) variations. We attribute this deficiency to two fundamental limitations in simulation and optimization. First, the reliance on coarse, oversimplified simulations (e.g., via CARLA) induces a significant domain gap, confining optimization to a biased feature space. Second, standard strategies targeting average performance result in a rugged loss landscape, leaving the camouflage vulnerable to configuration shifts.To bridge these gaps, we propose the Relightable Physical 3D Gaussian Splatting (3DGS) based Attack framework (R-PGA). Technically, to address the simulation fidelity issue, we leverage 3DGS to ensure photo-realistic reconstruction and augment it with physically disentangled attributes to decouple intrinsic material from lighting. Furthermore, we design a hybrid rendering pipeline that leverages precise Relightable 3DGS for foreground rendering, while employing a pre-trained image translation model to synthesize plausible relighted backgrounds that align with the relighted foreground.To address the optimization robustness issue, we propose the Hard Physical Configuration Mining (HPCM) module, designed to actively mine worst-case physical configurations and suppress their corresponding loss peaks. This strategy not only diminishes the overall loss magnitude but also effectively flattens the rugged loss landscape, ensuring consistent adversarial effectiveness and robustness across varying physical configurations.

Tianrui Lou, Siyuan Liang, Jiawei Liang +2

School of Cyber Science and Technology, Shenzhen Campus, Sun Yat-sen University, Shenzhen 518107, China · School of Computing, National University of Singapore, 117417, Singapore · School of Intelligent Systems Engineering, Shenzhen Campus, Sun Yat-sen University, Shenzhen 518107, China

Physical Adversarial Attacks Consistent Adversarial Attacks Autonomous Driving 3D Gaussian Splatting

Mar 26, 2026

Vega: Learning to Drive with Natural Language Instructions

Vision-language-action models have reshaped autonomous driving to incorporate languages into the decision-making process. However, most existing pipelines only utilize the language modality for scene descriptions or reasoning and lack the flexibility to follow diverse user instructions for personalized driving. To address this, we first construct a large-scale driving dataset (InstructScene) containing around 100,000 scenes annotated with diverse driving instructions with the corresponding trajectories. We then propose a unified Vision-Language-World-Action model, Vega, for instruction-based generation and planning. We employ the autoregressive paradigm to process visual inputs (vision) and language instructions (language) and the diffusion paradigm to generate future predictions (world modeling) and trajectories (action). We perform joint attention to enable interactions between the modalities and use individual projection layers for different modalities for more capabilities. Extensive experiments demonstrate that our method not only achieves superior planning performance but also exhibits strong instruction-following abilities, paving the way for more intelligent and personalized driving systems.

Sicheng Zuo, Yuxuan Li, Wenzhao Zheng +3

Tsinghua University · GigaAI

Autonomous Driving Autonomous Driving Planning Vision-Language-Action Models World Models for Autonomous Driving Autonomous Driving Datasets

Mar 26, 2026

Drive My Way: Preference Alignment of Vision-Language-Action Model for Personalized Driving

Human driving behavior is inherently personal, which is shaped by long-term habits and influenced by short-term intentions. Individuals differ in how they accelerate, brake, merge, yield, and overtake across diverse situations. However, existing end-to-end autonomous driving systems either optimize for generic objectives or rely on fixed driving modes, lacking the ability to adapt to individual preferences or interpret natural language intent. To address this gap, we propose Drive My Way (DMW), a personalized Vision-Language-Action (VLA) driving framework that aligns with users' long-term driving habits and adapts to real-time user instructions. DMW learns a user embedding from our personalized driving dataset collected across multiple real drivers and conditions the policy on this embedding during planning, while natural language instructions provide additional short-term guidance. Closed-loop evaluation on the Bench2Drive benchmark demonstrates that DMW improves style instruction adaptation, and user studies show that its generated behaviors are recognizable as each driver's own style, highlighting personalization as a key capability for human-centered autonomous driving. Our data and code are available at https://dmw-cvpr.github.io/.

Zehao Wang, Huaide Jiang, Shuaiwu Dong +3

University of California, Riverside · University of Michigan

Autonomous Driving Autonomous Driving Planning VLMs for Autonomous Driving

Mar 26, 2026

Can Users Specify Driving Speed? Bench2Drive-Speed: Benchmark and Baselines for Desired-Speed Conditioned Autonomous Driving

End-to-end autonomous driving (E2E-AD) has achieved remarkable progress. However, one practical and useful function has been long overlooked: users may wish to customize the desired speed of the policy or specify whether to allow the autonomous vehicle to overtake. To bridge this gap, we present Bench2Drive-Speed, a benchmark with metrics, dataset, and baselines for desired-speed conditioned autonomous driving. We introduce explicit inputs of users' desired target-speed and overtake/follow instructions to driving policy models. We design quantitative metrics, including Speed-Adherence Score and Overtake Score, to measure how faithfully policies follow user specifications, while remaining compatible with standard autonomous driving metrics. To enable training of speed-conditioned policies, one approach is to collect expert demonstrations that strictly follow speed requirements, an expensive and unscalable process in the real world. An alternative is to adapt existing regular driving data by treating the speed observed in future frames as the target speed for training. To investigate this, we construct CustomizedSpeedDataset, composed of 2,100 clips annotated with experts demonstrations, enabling systematic investigation of supervision strategies. Our experiments show that, under proper re-annotation, models trained on regular driving data perform comparably to on expert demonstrations, suggesting that speed supervision can be introduced without additional complex real-world data collection. Furthermore, we find that while target-speed following can be achieved without degrading regular driving performance, executing overtaking commands remains challenging due to the inherent difficulty of interactive behaviors. All code, datasets and baselines are available at https://github.com/Thinklab-SJTU/Bench2Drive-Speed

Yuqian Shao, Xiaosong Jia, Langechuan Liu +1

Sch. of Computer Science & Sch. of Artificial Intelligence, Shanghai Jiao Tong University · Institute of Trustworthy Embodied AI (TEAI), Fudan University · NVIDIA

Autonomous Driving Autonomous Driving Benchmarks End-to-End Autonomous Driving Autonomous Driving Planning Autonomous Driving Datasets

Mar 25, 2026

Goal-Oriented Reactive Simulation for Closed-Loop Trajectory Prediction

Current trajectory prediction models are primarily trained in an open-loop manner, which often leads to covariate shift and compounding errors when deployed in real-world, closed-loop settings. Furthermore, relying on static datasets or non-reactive log-replay simulators severs the interactive loop, preventing the ego agent from learning to actively negotiate surrounding traffic. In this work, we propose an on-policy closed-loop training paradigm optimized for high-frequency, receding horizon ego prediction. To ground the ego prediction in a realistic representation of traffic interactions and to achieve reactive consistency, we introduce a goal-oriented, transformer-based scene decoder, resulting in an inherently reactive training simulation. By exposing the ego agent to a mixture of open-loop data and simulated, self-induced states, the model learns recovery behaviors to correct its own execution errors. Extensive evaluation demonstrates that closed-loop training significantly enhances collision avoidance capabilities at high replanning frequencies, yielding relative collision rate reductions of up to 27.0% on nuScenes and 79.5% in dense DeepScenario intersections compared to open-loop baselines. Additionally, we show that a hybrid simulation combining reactive with non-reactive surrounding agents achieves optimal balance between immediate interactivity and long-term behavioral stability.

Harsh Yadav, Tobias Meisen

University of Wuppertal, 42119, Germany

Trajectory Prediction Autonomous Driving Autonomous Driving Simulation

Mar 25, 2026

Uncertainty-Aware Vision-based Risk Object Identification via Conformal Risk Tube Prediction

We study object importance-based vision risk object identification (Vision-ROI), a key capability for hazard detection in intelligent driving systems. Existing approaches make deterministic decisions and ignore uncertainty, which could lead to safety-critical failures. Specifically, in ambiguous scenarios, fixed decision thresholds may cause premature or delayed risk detection and temporally unstable predictions, especially in complex scenes with multiple interacting risks. Despite these challenges, current methods lack a principled framework to model risk uncertainty jointly across space and time. We propose Conformal Risk Tube Prediction, a unified formulation that captures spatiotemporal risk uncertainty, provides coverage guarantees for true risks, and produces calibrated risk scores with uncertainty estimates. To conduct a systematic evaluation, we present a new dataset and metrics probing diverse scenario configurations with multi-risk coupling effects, which are not supported by existing datasets. We systematically analyze factors affecting uncertainty estimation, including scenario variations, per-risk category behavior, and perception error propagation. Our method delivers substantial improvements over prior approaches, enhancing vision-ROI robustness and downstream performance, such as reducing nuisance braking alerts. For more qualitative results, please visit our project webpage: https://hcis-lab.github.io/CRTP/

Kai-Yu Fu, Yi-Ting Chen

National Yang Ming Chiao Tung University

Autonomous Driving Perception Autonomous Driving Conformal Risk Control Autonomous Driving Safety Evaluation Object Detection