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cs.RO2025

Zero-Human Demonstration End-to-end Autonomous Driving with Trajectory Scorer

Zhenxin Li, Nadine Chang, Wenhao Yao +9

Human demonstrations are widely considered the cornerstone of end-to-end (E2E) autonomous driving despite human demonstration's scarcity for long-tail and safety-critical scenarios…

cs.CV2025

DriveCritic: Towards Context-Aware, Human-Aligned Evaluation for Autonomous Driving with Vision-Language Models

Jingyu Song, Zhenxin Li, Shiyi Lan +6

Benchmarking autonomous driving planners to align with human judgment remains a critical challenge, as state-of-the-art metrics like the Extended Predictive Driver Model Score (EPD…

cs.RO2025

Generalized Trajectory Scoring for End-to-end Multimodal Planning

Zhenxin Li, Wenhao Yao, Zi Wang +7

End-to-end multi-modal planning is a promising paradigm in autonomous driving, enabling decision-making with diverse trajectory candidates. A key component is a robust trajectory s…

cs.RO2025

DriveSuprim: Towards Precise Trajectory Selection for End-to-End Planning

Wenhao Yao, Zhenxin Li, Shiyi Lan +4

Autonomous vehicles must navigate safely in complex driving environments. Imitating a single expert trajectory, as in regression-based approaches, usually does not explicitly asses…

cs.CV2025

MDP: Multidimensional Vision Model Pruning with Latency Constraint

Xinglong Sun, Barath Lakshmanan, Maying Shen +3

Current structural pruning methods face two significant limitations: (i) they often limit pruning to finer-grained levels like channels, making aggressive parameter reduction chall…

cs.RO2025

Enhancing Autonomous Driving Safety with Collision Scenario Integration

Zi Wang, Shiyi Lan, Xinglong Sun +4

Autonomous vehicle safety is crucial for the successful deployment of self-driving cars. However, most existing planning methods rely heavily on imitation learning, which limits th…

cs.LG2025

Advancing Weight and Channel Sparsification with Enhanced Saliency

Xinglong Sun, Maying Shen, Hongxu Yin +3

Pruning aims to accelerate and compress models by removing redundant parameters, identified by specifically designed importance scores which are usually imperfect. This removal is…