arXiv AI June 16, 2026 By Anna-Lena Schlamp, Jeremias Gerner, Klaus Bogenberger, Werner Huber, Stefanie Schmidtner

ROSA-RL: Uncertainty-Aware Roundabout Optimized Speed Advisory with Reinforcement Learning

arXiv:2606. 16558v1 Announce Type: new Abstract: Roundabouts challenge automated driving in mixed traffic, as heterogeneous and non-deterministic human behavior, unknown driving intentions, and high interaction complexity create uncertainty about whether the conflict zone will be blocked or available at the moment of entry.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv AI.

arXiv AI

Jun 10

Uncertainty-Aware Motion Planning for Autonomous Driving in Mixed Traffic Environment

arXiv:2606. 09958v1 Announce Type: cross Abstract: In mixed-traffic environments where autonomous and human-driven vehicles may co-exist, motion planning for autonomous vehicles requires anticipating the future behaviors of surrounding human drivers.

By Ming Cheng, Hao Chen, Ziyi Yang, Ziluowen Luo, Senzhang Wang

agents reinforcement-learning robotics safety

arXiv Machine Learning

Aug 10

Game-Theoretic Inverse Reinforcement Learning for Modeling Competitive Human Driving: A Cut-in Prediction Study

arXiv:2608. 06445v1 Announce Type: cross Abstract: Capturing the strategic decision-making inherent in competitive human driving is critical for autonomous vehicle safety and traffic simulation.

By Yu Song

agents reinforcement-learning benchmarks safety

arXiv AI

Jun 6

RiskFlow: Fast and Faithful Safety-Critical Traffic Scenario Generation

arXiv:2606. 06423v1 Announce Type: cross Abstract: Safety-critical traffic scenario generation is essential for evaluating autonomous driving systems under rare but high-risk interactions.

By Qi Lan, Yining Tang, Yu Shen, Yi Zhou, Yuhao Wei, Jie Li, Guofa Li

agents diffusion robotics safety

arXiv AI

Jul 1

DSIP: A Dynamic Coordination Planner for Signal-Free Intersections using Diffusion-Model-Based Multi-Agent Motion Planning

arXiv:2606. 30694v1 Announce Type: cross Abstract: Traffic signal control at urban intersections inherently introduces stop-and-go behavior, resulting in increased delays and reduced traffic efficiency, especially under high traffic demand.

By Qian Hu, Haoyang Peng, Songan Zhang, Ming Yang, Hongtei Eric Tseng

agents diffusion benchmarks

Hugging Face Trending Papers

Jun 22

A Generative Model for Closed-Loop Microsimulation of Signalized Intersections

Traffic microsimulators rely on hand-crafted behavior models that reproduce aggregate flow but miss the heterogeneous interactions between vehicles at signalized intersections. Learned trajectory predictors capture richer interactions but are short-horizon and tend to be unstable when run in closed loop.

llms benchmarks safety

arXiv Machine Learning

Jul 20

PRISA: Proactive Infrastructure LiDAR Framework for Intersection Safety Assessment

arXiv:2607. 16156v1 Announce Type: new Abstract: Urban intersections are among the most hazardous locations in road networks, posing significant risks to vehicles and vulnerable road users (VRUs) such as pedestrians and cyclists.

By Tam Bang, Hussam Abubakr, Emiliano de la Garza Villarreal, Truc Phuong Nguyen, Austin Harris, Toru Hirano, Mina Sartipi, Yunfei Xu, Hoang H. Nguyen

agents safety

Related stories

Uncertainty-Aware Motion Planning for Autonomous Driving in Mixed Traffic Environment

Game-Theoretic Inverse Reinforcement Learning for Modeling Competitive Human Driving: A Cut-in Prediction Study

RiskFlow: Fast and Faithful Safety-Critical Traffic Scenario Generation

DSIP: A Dynamic Coordination Planner for Signal-Free Intersections using Diffusion-Model-Based Multi-Agent Motion Planning

A Generative Model for Closed-Loop Microsimulation of Signalized Intersections

PRISA: Proactive Infrastructure LiDAR Framework for Intersection Safety Assessment