Long-Tail Autonomous Driving

Latest papers

11 papers1 paper in the last four weeks, against 1 the four weeks before. 0.0% of all new papers.

AnchorReasoning: A Visual Grounding and Causal Reasoning Dataset in Long-Tail Autonomous Driving Scenarios
Sep 23, 2026Zhipeng Bao, Wenjie Zhao, Tianle Zhu +4Autonomous Driving Datasets Long-Tail Autonomous Driving
School of Environmental, Civil, Agricultural and Mechanical Engineering, University of Georgia · School of Computing, University of Georgia
CoLT-Drive: Counterfactual Long-Tail Benchmarking and Knowledge-Preserving Adaptation for Driving Affordance Prediction
Aug 31, 2026Zhengxu Tang, Guofeng Cui, Ziyu Gong +8Autonomous Driving Benchmarks VLMs for Autonomous Driving
NVIDIA
OpenLongTail: Generative Scaling of Long-Tail Driving Data
Jul 10, 2026Lulin Liu, Nuo Chen, Yan Wang +15Long-Tail Autonomous Driving Camera-Controlled Video Generation
1Texas A&M University · 2NVIDIA · 3UW–Madison +6
Agent-driven Long-tail Simulation for Autonomous Driving
Jul 5, 2026Junru Gu, Lijin Yang, Jianing Huang +3Autonomous Driving Benchmarks Language Model-Based Control
1IIIS, Tsinghua University · 2Bosch Research
nuReasoning: A Reasoning-Centric Dataset and Benchmark for Long-Tail Autonomous Driving
May 29, 2026Zhiyu Huang, Johnson Liu, Rui Song +13Autonomous Driving Datasets Autonomous Driving Benchmarks
Xuewei · University of California, Los Angeles · 2Motional
CLAP: Contrastive Latent-space Prompt Optimization for End-to-end Autonomous Driving
May 17, 2026Ruiyang Zhu, Yuehan He, Boyuan Zheng +4Long-Tail Autonomous Driving VLMs for Autonomous Driving
University of Michigan · University of Arizona
Beyond the Beep: Scalable Collision Anticipation and Real-Time Explainability with BADAS-2.0
Apr 7, 2026Roni Goldshmidt, Hamish Scott, Lorenzo Niccolini +1Traffic Accident Anticipation Long-Tail Autonomous Driving
Nexar AI
Reasoning models do not yet follow their reasoning in autonomous driving: The KITScenes LongTail Dataset
Mar 24, 2026Royden Wagner, Omer Sahin Tas, Jaime Villa +20Long-Tail Autonomous Driving Autonomous Driving
Karlsruhe Institute of Technology (KIT) · FZI Research Center for Information Technology · Carlos III University of Madrid +5
ADV-0: Closed-Loop Min-Max Adversarial Training for Long-Tail Robustness in Autonomous Driving
Mar 16, 2026Tong Nie, Yihong Tang, Junlin He +5Long-Tail Autonomous Driving Adversarial Training
The Hong Kong Polytechnic University, Hong Kong SAR, China · Tongji University, Shanghai, China · McGill University, Montreal, QC, Canada
HERMES: A Holistic End-to-End Risk-Aware Multimodal Embodied System with Vision-Language Models for Long-Tail Autonomous Driving
Feb 1, 2026Weizhe Tang, Junwei You, Jiaxi Liu +7Long-Tail Autonomous Driving VLMs for Autonomous Driving
Department of Civil and Environmental Engineering, University of Wisconsin–Madison, Madison, WI 53711 USA
Zero-Human Demonstration End-to-end Autonomous Driving with Trajectory Scorer
Oct 28, 2025Zhenxin Li, Nadine Chang, Wenhao Yao +9Long-Tail Autonomous Driving Reinforcement Learning
Institute of Trustworthy Embodied AI, Fudan University · *Work done during an internship at NVIDIA. · NVIDIA +3

Sep 23, 2026J/K move · Enter open · S save

AnchorReasoning: A Visual Grounding and Causal Reasoning Dataset in Long-Tail Autonomous Driving Scenarios

Zhipeng Bao, Wenjie Zhao, Tianle Zhu, Haohua Que+3

School of Environmental, Civil, Agricultural and Mechanical Engineering, University of Georgia · School of Computing, University of Georgia

Autonomous Driving Datasets Long-Tail Autonomous Driving Autonomous Driving VLM Reasoning Visually Grounded Reasoning Visual Grounding

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Vision-language models (VLMs) offer a promising approach to long-tail autonomous driving, but existing driving datasets provide limited supervision for connecting decision-critical visual evidence with reasoning and planning. We introduce AnchorReasoning, a visually grounded reasoning dataset built on WOD-E2E, containing 416,119 annotated frames and 395,379 decision-critical elements across four major categories and 19 fine-grained types. Each frame is organized as a visually grounded chain-of-thought (VG-CoT) that links decision-critical element identification and localization, element attributes and implications, driving-action rationale, and action and trajectory planning. We further develop a curriculum supervised fine-tuning strategy that progressively learns these hierarchical capabilities, together with an object-size-aware grounding metric for evaluating localization quality. Experiments across eight general-purpose, embodied-AI, and AV-specific backbones show that VG-CoT supervision improves grounded reasoning and trajectory prediction. Across models, 5-s ADE and FDE decrease by 7.84 and 11.86, while RFS Frame and Cluster improve by 1.66 and 1.70. These gains are achieved with 18.5 fewer reasoning tokens and 0.32 s/frame lower inference latency on average, demonstrating the value of visually grounded, decision-focused supervision for VLM reasoning and planning in long-tail autonomous driving.