18 papers
QuantV2X: A Fully Quantized Multi-Agent System for Cooperative Perception
Seth Z. Zhao, Huizhi Zhang, Zhaowei Li +11
Cooperative perception through Vehicle-to-Everything (V2X) communication offers significant potential for enhancing vehicle perception by mitigating occlusions and expanding the fi…
nuReasoning: A Reasoning-Centric Dataset and Benchmark for Long-Tail Autonomous Driving
Zhiyu Huang, Johnson Liu, Rui Song +13
Reasoning is essential for autonomous driving (AD) in long-tail scenarios, where vehicles must apply commonsense knowledge, understand spatial relations, infer agent interactions,…
MDrive: Benchmarking Closed-Loop Cooperative Driving for End-to-End Multi-agent Systems
Marco Coscoy, Zewei Zhou, Seth Z. Zhao +9
Vehicle-to-Everything (V2X) communication has emerged as a promising paradigm for autonomous driving, enabling connected agents to share complementary perception information and ne…
ConFixGS: Learning to Fix Feedforward 3D Gaussian Splatting with Confidence-Aware Diffusion Priors in Driving Scenes
Rui Song, Tianhui Cai, Markus Gross +5
Feedforward 3D Gaussian Splatting (3DGS) often struggles in trajectory-based sparse-view driving scenes. Existing Gaussian repair methods mainly target optimization-based 3DGS, whi…
SpanVLA: Efficient Action Bridging and Learning from Negative-Recovery Samples for Vision-Language-Action Model
Zewei Zhou, Ruining Yang, Xuewei +8
Vision-Language-Action (VLA) models offer a promising autonomous driving paradigm for leveraging world knowledge and reasoning capabilities, especially in long-tail scenarios. Howe…
BridgeSim: Unveiling the OL-CL Gap in End-to-End Autonomous Driving
Seth Z. Zhao, Luobin Wang, Hongwei Ruan +13
Open-loop (OL) to closed-loop (CL) gap (OL-CL gap) exists when OL-pretrained policies scoring high in OL evaluations fail to transfer effectively in closed-loop (CL) deployment. In…