Autonomous Vehicle Technology and Safety

Últimos artículos

• RECAST: From Log Replay to Closed-Loop Driving Simulation with View-Complete Actors

  Zijun Zhao, Liewen Liao, Kang Shen, Songan Zhang, Ming Yang · 28 de septiembre de 2026

  Closed-loop driving simulation requires rendered observations to remain reliable as the ego vehicle and surrounding actors move beyond their recorded trajectories, exposing views absent from the source log. Existing data-driven simulators reconstruct dynamic actors from sparse observations, which ca…

• WALT: Learning World-Model-Aligned Latent Trajectories for Autonomous Driving

  Mingkai Jia, Jiaxin Guo, Zhijian Shu, Jiawei Xu, Mingxiao Li, Jintao Cheng, Ping Tan, Wei Yin · 28 de septiembre de 2026

  Driving world models learn rich predictive representations of the surrounding environment from visual observations, yet accurate visual prediction does not necessarily translate into effective trajectory planning. We argue that a key bottleneck lies in the mismatch between visual world states and ra…

• Guiding End-to-End Driving Models with Endpoint-Constrained Trajectory Optimization

  Brayden Zhang, Mahsa Golchoubian, Igor Gilitschenski, Boris Ivanovic, Kashyap Chitta · 28 de septiembre de 2026

  End-to-end driving policies are commonly trained through open-loop behavior cloning, yet they must ultimately operate in closed-loop when deployed on a vehicle, creating a fundamental mismatch between training and execution. Beyond the commonly studied effects of covariate shift and causal confusion…

• Evaluation Is All You Need for Multi-Modal Autonomous Driving

  Zeyu He, Shiqi Liu, Ke Chen, Yun Yan, Jinzi Wu, Dianqiao Lei, Sirui Wang, ShuRui Peng, Tao Chen, Zhuo Huang, Yu Wu, Yadong Shao, Zhichao Li, Ke Sun, Yang Guan, Keqiang Li, Shengbo Eben Li · 28 de septiembre de 2026

  Multi-modal planning is promising for autonomous driving by representing multiple plausible behaviors in ambiguous and long-tail scenarios. Existing methods mainly focus on improving trajectory multi-modality, enhancing trajectory representations, or reshaping the candidate distribution. Nevertheles…

• TrafficImag: A Benchmark for Counterfactual Roadside Traffic Video Generation

  Xiangyu Li, Tianyi Wang, Zhihao Dou, Christian Claudel, Zhaomiao Guo · 28 de septiembre de 2026

  Existing roadside traffic datasets support perception, forecasting, and visual question answering, but they do not evaluate counterfactual video generation, in which a selected actor is modified and the generated future should remain consistent with road topology and unrelated traffic. We introduce …

• Auditing Latent-Space Monitors for Autonomous Driving

  Nikhil Kamalkumar Advani, Vishwajeet Shivaji Hogale, Saurav Kumar · 28 de septiembre de 2026

  Runtime failure monitors can use a model's internal representations to anticipate failures. We audit this monitoring strategy across two autonomous-driving tasks: online vectorized map generation with LaneSegNet and end-to-end planning with VAD. We find that frame-level errors are predictable at inf…

• Safety-oriented pedestrian trajectory prediction at urban intersections using time-to-collision and crossing-zone context

  Erel Avineri, Yftach Gil, Yehudit Aperstein · 25 de septiembre de 2026

  Accurate pedestrian trajectory prediction is important for proactive road-safety applications, particularly at urban intersections where pedestrian motion is shaped by both vehicle interactions and crossing context. This study presents a safety-oriented trajectory-prediction framework that combines …

• Active Client Selection in Federated Trajectory Prediction with Uncertainty-Awareness and Heterogeneous Complexity

  Yiming Xie, Muzi Peng, Fei Miao, Ningfang Mi, Lili Su · 25 de septiembre de 2026

  Training sequence models such as transformers is now standard for autonomous vehicle trajectory prediction, yet assembling high-quality centralized datasets remains challenging because real-world trajectories are fragmented across regions and vehicles. Federated Learning (FL) offers a natural altern…

• HelloWorld: Towards Practical Applications of Generative Driving World Models

  Fan Lu, Hanshi Wang, Zijing Wang, Quan Feng, Zhi Wang, Shijie Chen, Xianming Zeng, Yujian Zhang, Jiazhe Wang, Xin Zha, Kai Wang, Zhijie Zhao, Lin Zhu, Tianyi Yang, Yucheng Xu, Tao Ji, Haodong Zhang, Zhipeng Zhang, Peixi Peng, Guang Chen, Xingliang Liu, Lei Yang, Jianyun Xu · 25 de septiembre de 2026

  Driving world models provide a promising route toward scalable counterfactual data generation and interactive simulation beyond recorded driving logs. Realizing this potential requires a system that can generalize across diverse scenes, respond faithfully to prescribed controls, generate coherent mu…

• S2Planner: Multi-Scale Semantic Planner for End-to-End Autonomous Driving

  Zhaowei Lu, Liguo Zhou, Yujie Guo, Lei Yu, Alois Knoll · 25 de septiembre de 2026

  We present S2Planner, a trajectory planner that combines three front-facing cameras with ego-motion history and the current driving command. A fine-tuned DINOv3 backbone and a Spatial Tuning Adapter produce multi-scale image features; a coarse-to-fine decoder then uses trajectory self-attention and …

• Less Language, More Latents: Annotation-Efficient VLAs for Driving

  Alexey Zakharov, Kemal Oksuz, Puneet K. Dokania · 24 de septiembre de 2026

  Vision-language-action models (VLA) promise human-steerable autonomous driving, but their training is bottlenecked by the scarcity of frames paired with natural-language instructions: while camera streams and expert trajectories are logged at scale, language annotations (e.g., turn left at the inter…

• ForeDrive: Foresight-Guided End-to-End Autonomous Driving with a Planning-Relevant Latent World Model

  Sinuo Wang, Zichong Gu, Yuhan Huang, Wenxin Wen, Xun Yang, Yiqing Zhang, Xingyu Zhang, Ningyu Che, Jie Ling, Qiankun Yu, Wei Liu, Jing Xu, Xinggang Wang · 24 de septiembre de 2026

  Existing latent world models are typically optimized for future predictability, yet the resulting representations are not necessarily useful for planning in autonomous driving. Predictions are commonly used for pretraining or auxiliary supervision rather than as direct conditioning signals for traje…

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

  Zhipeng Bao, Wenjie Zhao, Tianle Zhu, Haohua Que, Chence Yang, Geng Yuan, Qianwen Li · 24 de septiembre de 2026

  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 …

• RoadOcc Learns When to Persist, Transport, or Refresh Memory for Roadside Occupancy Prediction

  Xiaokai Bai, Lei Yang, Songkai Wang, Lianqing Zheng, Si-Yuan Cao, Hui-liang Shen · 24 de septiembre de 2026

  Fixed roadside cameras repeatedly observe a stable scene overlaid by sparse moving traffic. Temporal memory can recover weak observations, but reusing moving evidence at stale locations can corrupt occupancy predictions. Motion compensation addresses displacement, while reliance on the resulting his…

• Destination Support Restoration for Finite-Set Multimodal Trajectory Prediction

  Fengrui Liu, Jiajun Peng, Duo Peng, Feng Liu · 23 de septiembre de 2026

  Robots operating around pedestrians often reason over a finite set of predicted human futures. Repeated online updates can concentrate this limited prediction budget on dominant destinations and leave plausible alternatives underrepresented or absent, removing those alternatives from the finite repr…

• Correcting Learning-based Perception for Safety

  Yan Miao, Hussein Darir, Sayan Mitra · 22 de septiembre de 2026

  Learning-enabled perception is important in many autonomous systems. Unlike traditional sensors, the boundary where ML perception does or does not work is poorly characterized. Incorrect perception can lead to unsafe or overtly conservative downstream control actions. In this paper, we propose a two…

• Relationally Grounded Latent World Models for Autonomous Driving

  Fabian Schmidt, Markus Enzweiler, Abhinav Valada · 22 de septiembre de 2026

  Latent world models learn predictive representations for autonomous driving, but the relational semantics these states preserve often remain implicit. We investigate whether traffic scene graphs can serve as privileged semantic supervision for latent world representations. Building on LAW, we constr…

• Planning-Aligned Pretraining of BEV Representations with Sparse Action-Conditioned Targets for End-to-End Autonomous Driving

  Jaeha Song, Soonmin Hwang · 22 de septiembre de 2026

  End-to-end driving requires planning-relevant bird's-eye-view (BEV) representations, but existing pretraining approaches often rely on task annotations or dense scene reconstruction. We introduce PAVER, Planning-Aligned BEV Encoder Pretraining. From a single LiDAR sweep, PAVER constructs sparse risk…

• DriveReferee: Geometric Safety Verdicts Need Not Be Learned for Driving World-Action Models

  Fengcheng Yu, Dhruv Parikh, Junjie Ye, Maulik Bhatt, Thang Vu, Igor Vasiljevic, Vitor Guizilini, Yue Wang · 22 de septiembre de 2026

  Generative world-action models (WAMs) jointly generate future video and vehicle actions, while their action branches remain primarily optimized by expert imitation. Yet imitation provides no explicit closed-loop geometric verdict for generated trajectories, making verification important during both …

• Beyond the Leaderboard: Counterfactual Diagnosis of End-to-End and VLA Driving Policies Under Domain Shift

  Ruolin Yang, Zilin Huang, Buoyue Wang, Zhengyang Wan, Yuhao Luo, Zihao Sheng, Sikai Chen · 22 de septiembre de 2026

  End-to-end and vision-language-action (VLA) driving policies are compared by leaderboard rank, but a rank reports an outcome, not the behaviour behind it, so it predicts poorly how a policy will behave at a new site. On six released policies, rank on nuScenes open-loop error or on NAVSIM's leaderboa…

• Uni-PrevPredMap: Extending PrevPredMap to a Unified Framework of Prior-Informed Modeling for Online Vectorized HD Map Construction

  Nan Peng, Xun Zhou, Mingming Wang, Guisong Chen, Wenqi Xu · 21 de septiembre de 2026

  Safety-critical autonomous driving motivates the effective use of prior information. For online vectorized HD map construction, temporal predictions and cost-efficient HD map priors are two complementary yet individually imperfect sources. However, existing prior-informed approaches typically use on…

• PRIME: Perception Feedback with Situational Memory Embeddings in VLA Models

  Erik Deinzer, Naya Baslan, Luca Paparusso, Narunas Vaskevicius, Peter Knott, Luigi Palmieri · 21 de septiembre de 2026

  Current Vision-Language-Action (VLA) models for autonomous driving operate primarily through feedforward inference across the perception--reasoning--planning hierarchy. While modern architectures maintain temporal recurrence within the perceptual module, early perception remains blind to downstream …

• ZYT-World: A Real-Time Controllable World Model for Closed-Loop Autonomous-Driving Simulation

  Boni Hu, Xiong Wei, Haoming Huang, Yong Huang, Chenbo Wang, Yi Yang, Jiancheng Wang, Ruicheng Zhu, Zhimin Yang, Guanglai Liu, Qiaowan Jin, Dongzhuo Wang, Haiwei Kuang, Jiajun Fan, Yue Wu, Jiaxin Wei, Hao Sun, Feihong Yan, Wei Bi, Kaixuan Wang, Zichao Guo, Xiaozhi Chen · 21 de septiembre de 2026

  Generative world models offer controllable and repeatable closed-loop simulation for end-to-end and vision-language-action driving policies, but production deployment exposes three unresolved requirements: faithfully reproducing a mixed fisheye-pinhole rig at native resolutions; reconciling causal, …

• Driving on Registers, Reasoning on Risk: Risk-Aware Occupancy for Register-Based End-to-End Autonomous Driving

  Jiaxing Chen, Hengduo Zou, YuKai Qin, Yiren Zhao, Lidong Yu, Bolin Gao · 21 de septiembre de 2026

  Multimodal trajectory prediction improves behavioral coverage in end-to-end autonomous driving, but existing methods remain limited by sparse scene representations. Incomplete evidence leads to low-quality candidate generation and unreliable ranking among geometrically similar trajectories. On a reg…

• Risk-Aware Occupancy for Safety-Oriented End-to-End Autonomous Driving

  Jiaxing Chen, Hengduo Zou, Yiren Zhao, Bolin Gao · 21 de septiembre de 2026

  Sparse representation formulates the environment perception for the end-to-end driving system as a set of discrete elements like objects and lane lines. This formulation meets safety risks in crowded, occluded scenes dealing with unstructured obstacles, uncertain regions, and intricate interactions.…