The paper explores Behavior Cloning (BC) for autonomous driving using bird's‑eye view (BEV) inputs in simulated urban settings. It introduces a weighted BC approach that employs a kernel density estimator to adjust training sample weights, emphasizing rare but critical actions like stopping at red lights and accelerating at green lights. Experiments in the CARLA simulator show that this weighted method improves route completion and generalizes better to unseen test environments compared to standard BC.
By Eric Aislan Antonelo, Gustavo Claudio Karl Couto, Christian M\"oller, Pedro Henrique Fernandes
LightEMMA is a longitudinal evaluation framework that tests the autonomous driving performance of vision‑language models (VLMs) without fine‑tuning or prompt engineering. Using this protocol, the authors evaluated 15 VLMs from five major families on the nuScenes prediction benchmark and found that larger, more capable models do not consistently outperform earlier generations. The study identifies common failure modes such as overreliance on historical actions and difficulty reconciling conflicting visual cues, underscoring the need for domain‑specific adaptation to enhance VLM safety in autonomous driving.
By Zhijie Qiao, Haowei Li, Zhong Cao, Henry X. Liu
arXiv:2503.13938v3 Announce Type: replace-cross
Abstract: Comprehensive traffic scene understanding is a foundational capability for Intelligent Transportation Systems (ITS) underpinning applications...
By Qingyao Xu, Ya Zhang, Yanfeng Wang, Siheng Chen
arXiv:2606. 07708v1 Announce Type: cross Abstract: We introduce a dataset and benchmark for cross-view urban traffic perception built from synchronized ego-centric bicycle videos and aerial drone videos recorded at real urban intersections.
By Prakhar Bhardwaj, Simone Weikl, Kilian Mang, Elia Jonas Sandtner
MILER is an end‑to‑end reinforcement learning framework that achieves zero‑shot sim‑to‑real transfer for autonomous driving in unstructured environments. It uses a custom semantic mid‑level representation (MLR) simulator for offline training, and during deployment it processes real camera and LiDAR data with BEVFusion to produce a compatible bird’s‑eye‑view representation. The policy’s actions are applied via a trajectory‑alignment strategy, allowing the system to drive 17.3 km on a 3.0 km test track without human intervention, all running on a Jetson AGX Orin.
By Thomas Steinecker, Denis Trescher, Alexander Bienemann, Thorsten Luettel, Mirko Maehlisch
arXiv:2602. 23499v4 Announce Type: replace-cross Abstract: Collecting a high-quality dataset is a critical task that demands meticulous attention to detail, as overlooking certain aspects can render the entire dataset unusable.
By Tugrul Gorgulu, Atakan Dag, M. Esat Kalfaoglu, Halil Ibrahim Kuru, Baris Can Cam, Halil Ibrahim Ozturk, Ozsel Kilinc
Diffusion-2BC is a hybrid training method that combines a diffusion denoising objective with an auxiliary deterministic behavior‑cloning loss over a shared visual encoder. The auxiliary loss is used only during training, while inference remains diffusion‑based. Experiments on the Claw environment and CARLA navigation show that Diffusion‑2BC reduces mean mask‑distance error by about 10% compared to a diffusion baseline and by 85% compared to standard deterministic behavior cloning, and it enables the agent to travel farther and exhibit multimodal route choices.
By Bruno Maciel Machado, Eric Aislan Antonelo
arXiv:2610.31835v1 Announce Type: new
Abstract: Temporary traffic control redirects vehicles through work zones, requiring automated vehicles to connect an interpretation of the visible devices to a...
By Jiaxi Liu, Keshu Wu, Weizhe Tang, Bofeng Cao, Sikai Chen, Xiaopeng Li, Steven T. Parker, Bin Ran
arXiv:2609.22868v1 Announce Type: new
Abstract: End-to-end driving requires planning-relevant bird's-eye-view (BEV) representations, but existing pretraining approaches often rely on task annotations...
By Jaeha Song, Soonmin Hwang
arXiv:2603. 06576v2 Announce Type: replace-cross Abstract: The integration of Large Language Models (LLMs) into autonomous driving has attracted growing interest for their strong reasoning and semantic understanding abilities, which are essential for handling complex decision-making and long-tail scenarios.
By Thomas Monninger, Shaoyuan Xie, Qi Alfred Chen, Sihao Ding
arXiv:2609.36934v1 Announce Type: new
Abstract: Traffic signal control (TSC) is essential for improving urban mobility and reducing congestion. Although roadside cameras are widely deployed at signal...
By Pan Zhang, Siqi Lai, Kemu Dong, Hao Liu
RiskWorld is a risk‑aware world modeling framework that forecasts shared occupancy and selectively replaces planned trajectories in automated driving. It fuses spatial risk fields, temporal actor context, and visual bird’s‑eye‑view features, using flow‑guided evolution to transport occupancy and signed residuals to correct it. In open‑loop planning on nuScenes, RiskWorld achieves the lowest collision rate over a 3‑second horizon and the second‑best average L2 error, running at 11.5 FPS on a single NVIDIA RTX 4090.
By Rongxiang Zeng, Linsen Cai, Jiafu Zhang, Yijie Zhong, Yide Tao, Shuai Wang, Nan Zheng, Hai L. Vu, Alvaro Garcia Hernandez, Yongqi Dong