arXiv AI June 12, 2026 By Mahmoud Srewa, Praneetsai Iddamsetty, Mohammad Abdullah Al Faruque, Salma Elmalaki

PersonaDrive: Human-Style Retrieval-Augmented VLA Agents for Closed-Loop Driving Simulation

arXiv:2606. 12616v1 Announce Type: new Abstract: Closed-loop driving simulators typically populate their environments with non-ego traffic agents that behave largely the same way, produced either by rule-based traffic managers or by learned models trained toward a single behavioral mode.

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

arXiv AI

Jun 4

From Segments to Scenes: Temporal Understanding for Agentic Autonomous Driving via Vision-Language Models

arXiv:2512. 05277v4 Announce Type: replace-cross Abstract: Vision-Language Models (VLMs) are increasingly deployed as the perception and reasoning backbone of autonomous agents acting in the wild, with autonomous driving (AD) being one of the most safety-critical instances.

By Kevin Cannons, Saeed Ranjbar Alvar, Mohammad Asiful Hossain, Ahmad Rezaei, Mohsen Gholami, Alireza Heidarikhazaei, Zhou Weimin, Yong Zhang, Mohammad Akbari

llms agents robotics multimodal benchmarks safety

arXiv AI

Jun 2

From Segments to Scenes: Temporal Understanding in Autonomous Driving via Vision-Language Model

arXiv:2512. 05277v3 Announce Type: replace-cross Abstract: Vision-Language Models (VLMs) are increasingly deployed as the perception and reasoning backbone of autonomous agents acting in the wild, with autonomous driving (AD) being one of the most safety-critical instances.

By Kevin Cannons, Saeed Ranjbar Alvar, Mohammad Asiful Hossain, Ahmad Rezaei, Mohsen Gholami, Alireza Heidarikhazaei, Zhou Weimin, Yong Zhang, Mohammad Akbari

llms agents robotics multimodal benchmarks safety

arXiv Machine Learning

Jul 28

DriveDNA: A Large-Scale Multimodal Naturalistic Driving Dataset and Benchmark for Driving Style Identification

arXiv:2607. 23822v1 Announce Type: new Abstract: Driving style captures stable, driver-specific patterns in how a vehicle is driven.

By Yuhang Wang, Lingyao Li, Hao Zhou

multimodal benchmarks

arXiv AI

Jul 15

TerraZero: Procedural Driving Simulation for Zero-Demonstration Self-Play at Scale

arXiv:2607. 13028v1 Announce Type: cross Abstract: Training robust autonomous driving agents requires a simulator that is fast enough for reinforcement learning at scale, realistic enough to ground behavior in real-world map structure, and diverse enough to cover the safety-critical long tail that logged data rarely contains.

By Zhouchonghao Wu, Akshay Rangesh, Weixin Li, Wei-Jer Chang, Zachary Lee, Tim Wang, Wei Zhan

agents reinforcement-learning robotics benchmarks safety

arXiv AI

Jun 10

TaCarla: A comprehensive benchmarking dataset for end-to-end autonomous driving

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

agents computer-vision robotics benchmarks

arXiv AI

Jun 17

DriveJudge: Rethinking Autonomous Driving Evaluation with Vision-Language Models

arXiv:2606. 17362v1 Announce Type: cross Abstract: Autonomous driving has shifted towards end-to-end policy learning, where reliable, interpretable policy evaluation is a fundamental challenge as driving quality is highly context-dependent.

By Xinglong Sun, Kevin Xie, Jenny Schmalfuss, Despoina Paschalidou, Xiuming Zhang, Sanja Fidler, Kashyap Chitta, Jose M. Alvarez

llms agents robotics multimodal benchmarks

Related stories

From Segments to Scenes: Temporal Understanding for Agentic Autonomous Driving via Vision-Language Models

From Segments to Scenes: Temporal Understanding in Autonomous Driving via Vision-Language Model

DriveDNA: A Large-Scale Multimodal Naturalistic Driving Dataset and Benchmark for Driving Style Identification

TerraZero: Procedural Driving Simulation for Zero-Demonstration Self-Play at Scale

TaCarla: A comprehensive benchmarking dataset for end-to-end autonomous driving

DriveJudge: Rethinking Autonomous Driving Evaluation with Vision-Language Models