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cs.CV2024

Vista: A Generalizable Driving World Model with High Fidelity and Versatile Controllability

Shenyuan Gao, Jiazhi Yang, Li Chen +5

World models can foresee the outcomes of different actions, which is of paramount importance for autonomous driving. Nevertheless, existing driving world models still have limitati…

cs.RO2024

Reasoning Multi-Agent Behavioral Topology for Interactive Autonomous Driving

Haochen Liu, Li Chen, Yu Qiao +2

Autonomous driving system aims for safe and social-consistent driving through the behavioral integration among interactive agents. However, challenges remain due to multi-agent sce…

cs.RO2024

End-to-end Autonomous Driving: Challenges and Frontiers

Li Chen, Penghao Wu, Kashyap Chitta +3

The autonomous driving community has witnessed a rapid growth in approaches that embrace an end-to-end algorithm framework, utilizing raw sensor input to generate vehicle motion pl…

cs.CV2024

GenAD: Generalized Predictive Model for Autonomous Driving

Jiazhi Yang, Shenyuan Gao, Yihang Qiu +11

In this paper, we introduce the first large-scale video prediction model in the autonomous driving discipline. To eliminate the restriction of high-cost data collection and empower…

cs.CV2024

LaneSegNet: Map Learning with Lane Segment Perception for Autonomous Driving

Tianyu Li, Peijin Jia, Bangjun Wang +4

A map, as crucial information for downstream applications of an autonomous driving system, is usually represented in lanelines or centerlines. However, existing literature on map l…