Autonomous Driving Perception

Ask about this topic

Overview

Autonomous driving perception turns sensor data into estimates of what is around a vehicle, where things are, and how they are moving, supporting safe navigation and interaction. Common tasks include 2D and 3D object detection, lane and road segmentation, tracking, depth estimation, and 3D occupancy; approaches use cameras, LiDAR, radar, event sensors, or sensor fusion, often with bird’s-eye-view representations and temporal models. Methods are commonly evaluated on datasets such as nuScenes, Waymo, KITTI, Cityscapes, and OpenLane, with increasing attention to night, adverse weather, long range, occlusion, and sensor failure. A central challenge is improving real-world reliability and safety without excessive compute: learned fusion and vision-language models can add context, but must be grounded in accurate geometry and tested beyond clean, familiar benchmark conditions.

Latest papers

CardsList

381 papers34 papers in the last four weeks, down 3% on the four weeks before. 0.2% of all new papers.

Jan 13, 2026

Semantic Misalignment in Vision-Language Models under Perceptual Degradation

Vision-Language Models (VLMs) are increasingly deployed in autonomous driving and embodied AI systems, where reliable perception is critical for safe semantic reasoning and decision-making. While recent VLMs demonstrate strong performance on multimodal benchmarks, their robustness to realistic perception degradation remains poorly understood. In this work, we systematically study semantic misalignment in VLMs under controlled degradation of upstream visual perception, using semantic segmentation on the Cityscapes dataset as a representative perception module. We introduce perception-realistic corruptions that induce only moderate drops in conventional segmentation metrics, yet observe severe failures in downstream VLM behavior, including hallucinated object mentions, omission of safety-critical entities, and inconsistent safety judgments. To quantify these effects, we propose a set of language-level misalignment metrics that capture hallucination, critical omission, and safety misinterpretation, and analyze their relationship with segmentation quality across multiple contrastive and generative VLMs. Our results reveal a clear disconnect between pixel-level robustness and multimodal semantic reliability, highlighting a critical limitation of current VLM-based systems and motivating the need for evaluation frameworks that explicitly account for perception uncertainty in safety-critical applications.

Guo Cheng, Huang Li

VLM EvaluationVLM RobustnessObject Hallucination in VLMsAutonomous Driving Perception
AskPDF
Dec 16, 2025

4D-RaDiff: Latent Point Diffusion for 4D Radar Point Cloud Generation

Automotive radar has shown promising developments in environment perception due to its cost-effectiveness and robustness in adverse weather conditions. However, the limited availability of annotated radar data poses a significant challenge for advancing radar-based perception systems. To address this limitation, we propose a novel framework to generate 4D radar point clouds for training and evaluating object detectors. Unlike image-based diffusion, our method is designed to consider the sparsity and unique characteristics of radar point clouds by applying diffusion to a latent point cloud representation. Within this latent space, generation is controlled via conditioning at either the object or scene level. The proposed 4D-RaDiff converts unlabeled bounding boxes into high-quality radar annotations and transforms existing LiDAR point cloud data into realistic radar scenes. Experiments demonstrate that incorporating synthetic radar data of 4D-RaDiff as data augmentation method during training of object detection models consistently improves performance compared to training on real data only. In addition, pre-training on our synthetic radar data achieves competitive detection performance, providing a promising, scalable approach to reduce dependence on costly manual annotations.

Jimmie Kwok, Holger Caesar, Andras Palffy

Delft University of Technology, Netherlands · Perciv AI, Netherlands

Synthetic Data AugmentationPoint Cloud LearningAutonomous Driving PerceptionPoint Cloud GenerationDiffusion Models
AskPDF
Dec 9, 2025

LiDAS: Lighting-driven Dynamic Active Sensing for Nighttime Perception

Nighttime environments pose significant challenges for camera-based perception, as existing methods passively rely on the scene lighting. We introduce Lighting-driven Dynamic Active Sensing (LiDAS), a closed-loop active illumination system that combines off-the-shelf visual perception models with high-definition headlights. Rather than uniformly brightening the scene, LiDAS dynamically predicts an optimal illumination field that maximizes downstream perception performance, i.e., decreasing light on empty areas to reallocate it on object regions. LiDAS enables zero-shot nighttime generalization of daytime-trained models through adaptive illumination control. Trained on synthetic data and deployed zero-shot in real-world closed-loop driving scenarios, LiDAS enables +18.7% mAP50 and +5.0% mIoU over standard low-beam at equal power. It maintains performances while reducing energy use by 40%. LiDAS complements domain-generalization methods, further strengthening robustness without retraining. By turning readily available headlights into active vision actuators, LiDAS offers a cost-effective solution to robust nighttime perception.

Simon de Moreau, Andrei Bursuc, Hafid El-Idrissi +1

Valeo - Mines Paris PSL · Valeo · Mines Paris PSL

Autonomous Driving PerceptionActive SensingActive Visual Perception
AskPDF
Dec 9, 2025

Fast-BEV++: Fast by Algorithm, Deployable by Design

The advancement of vision-only BEV (Bird's-Eye-View) perception is hindered by the fundamental trade-off between perception accuracy and deployment efficiency. We introduce Fast-BEV++, resolving this tension through two principles: Fast by Algorithm and Deployable by Design. By decomposing view transformation into a hardware-oriented Index-Gather-Reshape pipeline, Fast-BEV++ eliminates custom kernels while achieving no less than 3 times speedup over baseline methods. Empirically, Fast-BEV++ establishes a new state-of-the-art accuracy-speed trade-off on nuScenes, achieving 0.488 NDS while sustaining real-time inference at over 134 FPS. In particular, depth supervision yields consistent and tangible performance gains, maintaining the highest accuracy among comparable methods. The decomposed architecture enables seamless real-time deployment on production-level platforms, eliminating hardware constraints without loss of efficiency. Code and models are released on the linked project page.

Yuanpeng Chen, Hui Song, Sheng Yang +5

iMotion Automotive Technology (Suzhou) Co., Ltd · School of Data Science, Fudan University · Independent Researcher

Autonomous Driving PerceptionBird's-Eye-View Representation3D Object Detection
AskPDF
Nov 23, 2025

UniFlow: Zero-Shot LiDAR Scene Flow for Autonomous Vehicles

LiDAR scene flow is the task of estimating per-point 3D motion between consecutive point clouds. Recent methods achieve centimeter-level accuracy on popular autonomous vehicle (AV) datasets, but are typically only trained and evaluated on a single sensor. In this paper, we aim to learn general motion priors that transfer to diverse and unseen LiDAR sensors. However, prior work in LiDAR semantic segmentation and 3D object detection demonstrate that naively training on multiple datasets yields worse performance than single dataset models. Interestingly, we find that this conventional wisdom does not hold for motion estimation, and that state-of-the-art scene flow methods greatly benefit from cross-dataset training without architectural modification. We posit that low-level tasks such as motion estimation may be less sensitive to sensor configuration; indeed, our analysis shows that models trained on fast-moving objects (e.g., from highway datasets) perform well on fast-moving objects, even across different datasets. Informed by our analysis, we propose UniFlow, a feedforward model that unifies and trains on multiple large-scale LiDAR scene flow datasets with diverse sensor placements and point cloud densities. Our frustratingly simple solution establishes a new state-of-the-art on Waymo and nuScenes, improving over prior work by 5.1% and 35.2% respectively. Moreover, UniFlow achieves state-of-the-art accuracy on unseen datasets like TruckScenes and AEVAScenes, outperforming prior dataset-specific models by 30.1% and 22.5% respectively.

Siyi Li, Qingwen Zhang, Ishan Khatri +4

University of Pennsylvania · KTH Royal Institute of Technology · StackAV +1

Point Cloud LearningAutonomous Driving PerceptionCross-Domain Generalization3D Scene Understanding
AskPDF
Nov 22, 2025

RoadBench: Benchmarking MLLMs on Fine-Grained Spatial Understanding and Reasoning under Urban Road Scenarios

Multimodal large language models (MLLMs) have demonstrated powerful capabilities in general spatial understanding and reasoning. However, their fine-grained spatial understanding and reasoning capabilities in complex urban scenarios have not received significant attention in the fields of both research and industry. To fill this gap, we focus primarily on road markings as a typical example of fine-grained spatial elements under urban scenarios, given the essential role of the integrated road traffic network they form within cities. Around road markings and urban traffic systems, we propose RoadBench, a systematic benchmark that comprehensively evaluates MLLMs' fine-grained spatial understanding and reasoning capabilities using Bird's-Eye View (BEV) and First-Person View (FPV) image inputs. This benchmark comprises eight tasks consisting of 3,040 strictly manually verified test cases, constructed from 2,137 unique BEV images and 721 unique FPV images collected from five Chinese cities with relatively consistent traffic conventions. These tasks form a systematic evaluation framework that bridges understanding at local spatial scopes to global reasoning. They not only test MLLMs' capabilities in recognition, joint understanding, and reasoning but also assess their ability to integrate image information with domain knowledge. After evaluating 20 mainstream MLLMs, we confirm that RoadBench is a challenging benchmark for MLLMs while revealing significant shortcomings in existing MLLMs' fine-grained spatial understanding and reasoning capabilities within urban scenarios. In certain tasks, their performance even falls short of simple rule-based or random selection baselines. These findings, along with RoadBench itself, will contribute to the comprehensive advancement of spatial understanding capabilities for MLLMs.

Jun Zhang, Xin Zhang, Jie Feng +5

Department of Electronic Engineering, BNRist, Tsinghua University, Beijing, China · Zhongguancun Academy · Amap, Alibaba Group, Beijing, China

VLM EvaluationSpatial Reasoning BenchmarksVisual Spatial ReasoningAutonomous Driving PerceptionFine-Grained Visual Perception
AskPDF
Nov 21, 2025

Benchmarking Nighttime Traffic Sign Recognition with Illumination-Adaptive Detection and Semantic Attribute Reasoning

Traffic signboards are vital for road safety and intelligent transportation systems. Yet, recognizing traffic signs at night remains underexplored due to the scarcity of real-world public datasets capturing low-light degradations and distractor classes. Existing benchmarks are predominantly daytime and do not reflect challenges such as headlight glare, motion blur, sensor noise, and vandalized or ambiguous signage. To address these gaps, we introduce INTSD, a large-scale nighttime traffic sign dataset collected across diverse regions of India. INTSD contains street-level images spanning 41 traffic signboard classes, multiple distractor categories, and varied lighting and weather conditions, designed to support both detection and fine-grained classification under nighttime scenarios. To benchmark INTSD, we conduct extensive evaluations using state-of-the-art detection and classification models under standardized protocols. Additionally, we present LENS-Net, a strong baseline that integrates an end-to-end adaptive illumination-aware detector with a multimodal classifier that fuses vision-language representations with soft semantic attribute reasoning over learnable shape and color embeddings. Experiments demonstrate that models trained exclusively on daytime data fail substantially under real nighttime conditions - a gap that is recovered once INTSD is introduced in training, even when controlling for data volume. These results validate INTSD as a complementary nighttime training resource and establish competitive baselines for future research. The code and dataset are publicly available.

Aditya Mishra, Akshay Agarwal, Haroon Lone

IISER Bhopal

Traffic Sign DetectionIntelligent Transportation SystemsAutonomous Driving PerceptionObject Detection
AskPDF
Nov 10, 2025

HENet++: Hybrid Encoding and Multi-task Learning for 3D Perception and End-to-end Autonomous Driving

Three-dimensional feature extraction and multi-task perception are fundamental components of modern autonomous driving systems. Although large image encoders, high-resolution inputs, and long temporal contexts can substantially improve representation quality and overall performance, jointly leveraging these strategies remains challenging due to prohibitive computational costs during both training and inference. Furthermore, different perception tasks often require distinct feature representations, making it difficult for a unified architecture to achieve end-to-end multi-task performance comparable to specialized single-task systems. To address these challenges, we propose HENet++, a unified framework for multi-task 3D perception and end-to-end autonomous driving that employs a hybrid image encoding strategy, using a large encoder for short-term frames and a lightweight encoder for long-term temporal context to strike a favorable balance between accuracy and efficiency. The framework jointly extracts dense background features and sparse foreground features, enabling task-specific representations that reduce cumulative errors and provide richer information for downstream prediction and planning modules. HENet++ is compatible with diverse 3D feature extraction pipelines and supports multi-modal inputs, including camera and radar data. Extensive experiments demonstrate state-of-the-art performance on the nuScenes multi-task 3D perception benchmark, achieving the lowest collision rate on the nuScenes planning benchmark and higher PDMS on the NAVSIM benchmark.

Zhongyu Xia, Zhiwei Lin, Yongtao Wang +1

Wangxuan Institute of Computer Technology, Peking University, Beijing, China. · University of California, Merced, USA.

Autonomous Driving PerceptionEnd-to-End Autonomous Driving3D Scene UnderstandingMulti-Task LearningAutonomous Driving Planning
AskPDF
Nov 10, 2025

DIAL-GS: Dynamic Instance Aware Reconstruction for Label-free Street Scenes with 4D Gaussian Splatting

Urban scene reconstruction is critical for autonomous driving, enabling structured 3D representations for data synthesis and closed-loop testing. Supervised approaches rely on costly human annotations and lack scalability, while current self-supervised methods often confuse static and dynamic elements and fail to distinguish individual dynamic objects, limiting fine-grained editing. We propose DIAL-GS, a novel dynamic instance-aware reconstruction method for label-free street scenes with 4D Gaussian Splatting. We first accurately identify dynamic instances by exploiting appearance-position inconsistency between warped rendering and actual observation. Guided by instance-level dynamic perception, we employ instance-aware 4D Gaussians as the unified volumetric representation, realizing dynamic-adaptive and instance-aware reconstruction. Furthermore, we introduce a reciprocal mechanism through which identity and dynamics reinforce each other, enhancing both integrity and consistency. Experiments on urban driving scenarios show that DIAL-GS surpasses existing self-supervised baselines in reconstruction quality and instance-level editing, offering a concise yet powerful solution for urban scene modeling.

Chenpeng Su, Wenhua Wu, Chensheng Peng +3

Shanghai Jiao Tong University, Shanghai 200240, China. · University of California, Berkeley, Berkeley, CA 94720, USA.

3D Gaussian SplattingSelf-Supervised LearningAutonomous Driving PerceptionDynamic Scene Reconstruction
AskPDF
Oct 31, 2025

MLPerf Automotive

We present MLPerf Automotive, the first standardized public performance benchmark for evaluating Machine Learning systems that are deployed for AI acceleration in automotive systems. Developed through a collaborative partnership within MLCommons, this benchmark addresses the need for standardized performance evaluation methodologies in automotive machine learning systems. Existing benchmark suites cannot be utilized for these systems since automotive workloads have unique constraints including sensor suites, safety, and real-time processing that distinguish them from the domains that previously introduced benchmarks target. Our implemented and adopted MLPerf Automotive benchmark is a framework for evaluation and methodology for benchmarking automotive systems with reproducible performance metrics. The benchmark consists of automotive perception tasks in 2D object detection, 2D semantic segmentation, 3D object detection, end-to-end driving, and an infotainment system application. We carefully curated and customized models for automotive use cases. We describe the methodology behind the benchmark design including the task selection, reference models, and submission rules. We also discuss the challenges involved in acquiring the datasets and the engineering efforts to develop the reference implementations. Our benchmark code is available at https://github.com/mlcommons/mlperf_automotive.

Radoyeh Shojaei, Predrag Djurdjevic, Mostafa El-Khamy +7

University of California, Davis · Arm · Samsung +5

End-to-End Autonomous DrivingBenchmark DesignAutonomous Driving Perception
AskPDF
Oct 30, 2025

AD-SAM: Adapting the Segment Anything Model for Semantic Segmentation in Autonomous Driving

This paper presents the Autonomous Driving Segment Anything Model (AD-SAM), a foundation-model adaptation framework for semantic segmentation in autonomous driving. AD-SAM combines a frozen Segment Anything Model (SAM) Vision Transformer (ViT-H) encoder with a trainable ResNet-50 encoder to integrate general visual representations with multi-scale, domain-specific spatial features. Features from the two encoders are integrated through deformable convolution and channel attention, followed by a multi-stage deformable decoder for semantic prediction. Training employs a hybrid objective combining Focal, Dice, Lovász-Softmax, and Surface losses. Experiments on Cityscapes and Berkeley DeepDrive 100K (BDD100K) show that AD-SAM outperforms SAM, Generalized SAM (G-SAM), and DeepLabV3 under a controlled training protocol. AD-SAM achieves 76.27% mIoU on Cityscapes and 64.74% on BDD100K, exceeding DeepLabV3 by 3.45 and 5.00 percentage points, respectively, with larger gains over the SAM-based baselines. Sample-size experiments reveal dataset-dependent behavior. In particular, AD-SAM performs strongly across training sizes on Cityscapes, while its advantage on the more heterogeneous BDD100K becomes pronounced with increased training data. When trained on Cityscapes and directly evaluated on BDD100K, AD-SAM achieves the highest cross-dataset retention (84.88%) among the evaluated models. AD-SAM also converges rapidly, while precomputed frozen SAM embeddings reduce training memory requirements. These findings demonstrate the potential of combining general foundation-model representations with domain-specific multi-scale features for accurate and robust autonomous-driving semantic segmentation.

Het Patel, Mario Camarena, Fatemeh Nazari +3

Department of Computer Science, The University of Texas Rio Grande Valley, Edinburg, TX 78539 USA · Department of Civil Engineering, The University of Texas Rio Grande Valley, Edinburg, TX 78539 USA · Department of Computer Science and Engineering, University of California Riverside, Riverside, CA 92521 USA

Vision Foundation Model AdaptationAutonomous Driving PerceptionSemantic Segmentation
AskPDF
Oct 29, 2025

BikeScenes: LiDAR Semantic Segmentation for Bicycles

The vulnerability of cyclists, exacerbated by the rising popularity of faster e-bikes, motivates adapting automotive perception technologies for bicycle safety. We use our multi-sensor SenseBike research platform to study 3D LiDAR semantic segmentation for bicycles. We introduce the novel BikeScenes-lidarseg Dataset, comprising 3021 consecutive LiDAR scans around the university campus of the TU Delft, semantically annotated for 29 dynamic and static classes. As an initial baseline study, we evaluate how a SemanticKITTI pre-trained FRNet model transfers to this bicycle-mounted solid-state LiDAR setting. Fine-tuning on BikeScenes increases mean Intersection-over-Union (mIoU) from 13.8% to 63.6% on our held-out subsequences. These results show the practical value of in-domain data for this platform, while also highlighting the need for larger bicycle datasets. We contribute BikeScenes as a resource for advancing research in cyclist-centric perception.

Denniz Goren, Holger Caesar

Autonomous Driving Perception3D Semantic Segmentation
AskPDF
Oct 24, 2025

AURASeg: Attention-Guided Upsampling with Residual-Assisted Boundary Refinement for Drivable-Area Segmentation

Free-space segmentation is essential for autonomous robots to identify drivable regions and navigate safely across indoor, outdoor, and road-scene environments. However, conventional encoder-decoder models often recover coarse region masks while losing the fine spatial information needed to localize drivable-area boundaries accurately. We propose Attention-Guided Upsampling with Residual-Assisted Boundary Refinement (AURASeg), a segmentation framework designed to preserve region-level accuracy while improving boundary quality. Built on a ResNet-18 encoder, AURASeg introduces an Attention Progressive Upsampling Decoder (APUD) that progressively combines semantic context with high-resolution spatial detail, together with a Residual Boundary Refinement Module (RBRM) that explicitly refines contour-sensitive features before final prediction. We evaluate AURASeg across indoor simulation, ground-robot imagery, and road-driving benchmarks. The results show that our proposed model remains competitive with established segmentation models on region-level metrics while providing particularly strong boundary localization, including in comparison with boundary-focused methods. Detailed ablations further demonstrate the role of the proposed decoding and refinement modules.

Narendhiran Vijayakumar

Autonomous Driving PerceptionBoundary-Aware SegmentationSemantic Segmentation
AskPDF
Oct 9, 2025

Injecting Hallucinations in Autonomous Vehicles: A Component-Agnostic Safety Evaluation Framework

Perception failures in autonomous vehicles (AV) remain a major safety concern because they can directly compromise decision-making and contribute to unsafe behavior. To study how these failures affect safety, researchers typically inject artificial faults into real-world or simulated components and observe the resulting outcomes. However, existing fault injection (FI) studies often target a single sensor within the machine perception (MP) module, resulting in siloed frameworks that are difficult to generalize or integrate into unified simulation environments. This work addresses that limitation by reframing perception failures as hallucinations, that is, false perceptions that distort an AV's situational awareness and may trigger unsafe control actions. Building on this concept, we propose a configurable, component-agnostic hallucination injection framework that induces six plausible hallucination types in an open-source simulation environment. A total of 18,356 simulations (8,695 baseline and 9,661 with hallucinations injected) were executed while AVs crossed an unsignalized intersection with transversal traffic. The results statistically validate the framework and quantify the impact of each hallucination type on collisions and on the minimum distance between the AV and the nearest vehicle, used as a continuous proxy for the safety margin. Certain hallucinations, especially missed detection, blind region, angular drift, phantom perception, and latency, significantly increase the odds of collision, while linear drift significantly reduces minimum distances without producing a statistically significant increase in collision odds in the evaluated scenario. The proposed framework provides a scalable and statistically grounded method for evaluating AV safety under perception distortions and supports future research on fault tolerance and resilient AV design.

Alexandre Moreira Nascimento, Gabriel Kenji Godoy Shimanuki, Lúcio Flavio Vismari +5

Escola Politécnica da Universidade de São Paulo, São Paulo 05508-010, Brazil · Center for Design Research, Department of Mechanical Engineering, Stanford, CA 94305 USA · Department of Communication, University of Miami, Coral Gables, FL 33124 USA +1

Autonomous Driving Safety EvaluationAutonomous DrivingAutonomous Driving PerceptionAutonomous Driving Simulation
AskPDF
Aug 15, 2025

OEL-AD: An Online Evolutive Learning Framework for Cross-Region Adaptive Autonomous Driving

Autonomous driving models have achieved great progress in recent years, yet most existing approaches remain static, following a train-then-deploy paradigm that lacks the ability to adjust internal parameters when encountering new environments. This limitation is particularly critical in cross-region scenarios, which are common in real-world deployments in autonomous driving where differences in road layouts, traffic patterns, and environmental conditions introduce distribution shifts. In this paper, we argue for a paradigm shift towards Online Evolutive Learning (OEL), a learning paradigm designed to enable adaptive, online model optimization through the interaction and coordination of intelligent components in dynamic environments. We introduce OEL-AD, a novel framework that instantiates this paradigm for autonomous driving. Specifically, OEL-AD leverages trajectory uncertainty from the planning module to determine whether adaptive updates are necessary. When triggered, the framework uses discrepancies between predicted agent behaviors and subsequent observations as a self-supervised learning signal to refine perception and prediction modules, thereby indirectly improving planning accuracy and safety. Experiments on the nuScenes dataset with a cross-region split between Singapore and Boston demonstrate that OEL-AD consistently improves perception and prediction performance. This enhancement in perception and prediction consequently results in improved planning capabilities, thereby confirming the promise of online evolutive learning as a fundamental approach for building adaptive and robust autonomous driving systems.

Jiayue Jin, Lang Qian, Jingyu Zhang +2

College of Intelligent Robotics and Advanced Manufacturing, Fudan University, Shanghai, China

Autonomous DrivingAdaptive LearningAutonomous Driving PerceptionDomain AdaptationAutonomous Driving Planning
AskPDF
May 27, 2025

CogAD: Cognitive-Hierarchy Guided End-to-End Autonomous Driving

While end-to-end autonomous driving has advanced significantly, prevailing methods remain fundamentally misaligned with human cognitive principles in both perception and planning. In this paper, we propose CogAD, a novel end-to-end autonomous driving model that emulates the hierarchical cognition mechanisms of human drivers. CogAD implements dual hierarchical mechanisms: global-to-local context processing for human-like perception and intent-conditioned multi-mode trajectory generation for cognitively-inspired planning. The proposed method demonstrates three principal advantages: comprehensive environmental understanding through hierarchical perception, robust planning exploration enabled by multi-level planning, and diverse yet reasonable multi-modal trajectory generation facilitated by dual-level uncertainty modeling. Extensive experiments on nuScenes and Bench2Drive demonstrate that CogAD achieves state-of-the-art performance in end-to-end planning, exhibiting particular superiority in long-tail scenarios and robust generalization to complex real-world driving conditions.

Zhennan Wang, Jianing Teng, Canqun Xiang +4

HAOMO.AI Technology Co., Ltd

End-to-End Autonomous DrivingAutonomous Driving PerceptionAutonomous Driving Planning
AskPDF
Mar 11, 2025

Simulating Automotive Radar with Lidar and Camera Inputs

Low-cost millimeter automotive radar has received more and more attention due to its ability to handle adverse weather and lighting conditions in autonomous driving. However, the lack of quality datasets hinders research and development. We report a new method that is able to simulate 4D millimeter wave radar signals including pitch, yaw, range, and Doppler velocity along with radar signal strength (RSS) using camera image, light detection and ranging (lidar) point cloud, and ego-velocity. The method is based on two new neural networks: 1) DIS-Net, which estimates the spatial distribution and number of radar signals, and 2) RSS-Net, which predicts the RSS of the signal based on appearance and geometric information. We have implemented and tested our method using open datasets from 3 different models of commercial automotive radar. The experimental results show that our method can successfully generate high-fidelity radar signals. Moreover, we have trained a popular object detection neural network with data augmented by our synthesized radar. The network outperforms the counterpart trained only on raw radar data, a promising result to facilitate future radar-based research and development.

Peili Song, Dezhen Song, Yifan Yang +2

Institute of Robotics and Automatic Information System, Nankai University, Tianjin 300350, China; Tianjin Key Laboratory of Intelligent Robotics, Tianjin 300350, China; and also with TBI center, Nankai University, Tianjin 300350, China · Department of Robotics, Mohamed Bin Zayed University of Artificial Intelligence (MBZUAI), Abu Dhabi, United Arab Emirates

Synthetic Data AugmentationMillimeter-Wave RadarmmWave Radar SensingAutonomous Driving PerceptionSynthetic Data Generation for RoboticsAutonomous Driving Simulation
AskPDF
Dec 17, 2024

Open-World Panoptic Segmentation

Robots need to be able to understand their surroundings in order to operate safely and robustly, and to interact with the surrounding environment. Robots deployed in unconstrained real-world scenarios must additionally be able to deal with novel situations and objects that have never been seen before. In this article, we tackle the problem of open-world panoptic segmentation, i.e., the task of discovering new semantic categories and new object instances at test time, while enforcing consistency among the categories that we incrementally discover. We present Con2MAV, a general method for open-world panoptic segmentation. Experiments across a wide range of datasets, from road scenes to underwater environments, highlight its compelling capabilities in open-world segmentation and its competitive performance on known classes. We will open-source the implementation of our approach upon acceptance. In addition, we propose PANIC (Panoptic ANomalies In Context), a benchmark for evaluating open-world segmentation tasks in autonomous driving scenarios. This dataset, recorded with a multi-modal sensor suite mounted on a car, and then manually annotated, provides high-quality, pixel-wise annotations of anomalous objects at both semantic and instance level. PANIC contains 800 images, more than 50 unknown classes, i.e., classes that do not appear in the training set, and over 4,000 object instances, providing a comprehensive benchmark for evaluating open-world segmentation methods in autonomous driving scenarios. We provide competitions for multiple open-world segmentation tasks on a hidden test set. Our dataset and competitions are available at https://www.ipb.uni-bonn.de/data/panic.

Matteo Sodano, Federico Magistri, Jens Behley +1

Center for Robotics, University of Bonn, Germany, and the Lamarr Institute for Machine Learning and Artificial Intelligence, Germany

Image SegmentationPanoptic SegmentationAutonomous Driving Perception
AskPDF
Dec 10, 2024

Test-time Correction: An Online 3D Detection System via Visual Prompting

This paper introduces Test-time Correction (TTC), an online 3D detection system designed to rectify test-time errors using various auxiliary feedback, aiming to enhance the safety of deployed autonomous driving systems. Unlike conventional offline 3D detectors that remain fixed during inference, TTC enables immediate online error correction without retraining, allowing autonomous vehicles to adapt to new scenarios and reduce deployment risks. To achieve this, we equip existing 3D detectors with an Online Adapter (OA) module -- a prompt-driven query generator for real-time correction. At the core of OA module are visual prompts: image-based descriptions of objects of interest derived from auxiliary feedback such as mismatches with 2D detections, road descriptions, or user clicks. These visual prompts, collected from risky objects during inference, are maintained in a visual prompt buffer to enable continuous correction in future frames. By leveraging this mechanism, TTC consistently detects risky objects, achieving reliable, adaptive, and versatile driving autonomy. Extensive experiments show that TTC significantly improves instant error rectification over frozen 3D detectors, even under limited labels, zero-shot settings, and adverse conditions. We hope this work inspires future research on post-deployment online rectification systems for autonomous driving.

Hanxue Zhang, Zetong Yang, Yanan Sun +3

Shanghai Jiao Tong University, Shanghai, China · Shanghai AI Lab, Shanghai, China · GAC R&D Center, Guangzhou, China +2

Autonomous Driving Perception3D Object Detection
AskPDF
Nov 3, 2024

ROAD-Waymo: A Large-Scale Action Awareness Dataset for Autonomous Driving

Autonomous Vehicle (AV) perception systems require more than simply seeing, via e.g., object detection or scene segmentation. They need a holistic understanding of what is happening within the scene for safe interaction with other road users. Few datasets exist for the purpose of developing and training algorithms to comprehend the actions of other road users. This paper presents ROAD-Waymo, an extensive dataset for the development and benchmarking of techniques for agent, action, location and event detection in road scenes, provided as a layer upon the (US) Waymo Open dataset. Considerably larger and more challenging than any existing dataset (and encompassing multiple cities), it comes with 198k annotated video frames, 54k agent tubes, 3.9M bounding boxes and a total of 12.4M labels. The integrity of the dataset has been confirmed and enhanced via a novel annotation pipeline designed for automatically identifying violations of requirements specifically designed for this dataset. As ROAD-Waymo is compatible with the original (UK) ROAD dataset, it provides the opportunity to tackle domain adaptation between real-world road scenarios in different countries within a novel benchmark: ROAD++.

Salman Khan, Izzeddin Teeti, Reza Javanmard Alitappeh +5

Oxford Brookes University · MAZUST · University of Oxford +2

Autonomous Driving DatasetsAutonomous Driving BenchmarksAutonomous DrivingAutonomous Driving Perception
AskPDF
Jun 21, 2021

EPMF: Efficient Perception-aware Multi-sensor Fusion for 3D Semantic Segmentation

We study multi-sensor fusion for 3D semantic segmentation that is important to scene understanding for many applications, such as autonomous driving and robotics. Existing fusion-based methods, however, may not achieve promising performance due to the vast difference between the two modalities. In this work, we investigate a collaborative fusion scheme called perception-aware multi-sensor fusion (PMF) to effectively exploit perceptual information from two modalities, namely, appearance information from RGB images and spatio-depth information from point clouds. To this end, we project point clouds to the camera coordinate using perspective projection, and process both inputs from LiDAR and cameras in 2D space while preventing the information loss of RGB images. Then, we propose a two-stream network to extract features from the two modalities, separately. The extracted features are fused by effective residual-based fusion modules. Moreover, we introduce additional perception-aware losses to measure the perceptual difference between the two modalities. Last, we propose an improved version of PMF, i.e., EPMF, which is more efficient and effective by optimizing data pre-processing and network architecture under perspective projection. Specifically, we propose cross-modal alignment and cropping to obtain tight inputs and reduce unnecessary computational costs. We then explore more efficient contextual modules under perspective projection and fuse the LiDAR features into the camera stream to boost the performance of the two-stream network. Extensive experiments on benchmark data sets show the superiority of our method. For example, on nuScenes test set, our EPMF outperforms the state-of-the-art method, i.e., RangeFormer, by 0.9% in mIoU. Our source code is available at https://github.com/ICEORY/PMF.

Mingkui Tan, Zhuangwei Zhuang, Sitao Chen +4

School of Software Engineering, South China University of Technology · Pazhou Laboratory, Guangzhou, China · School of Electronic and Information Engineering, South China University of Technology +2

Camera-LiDAR FusionAutonomous Driving Perception3D Semantic SegmentationMultimodal Sensor Fusion
AskPDF