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arXiv Robotics — research abstracts· Ruikai Li, Naiyu Gao, Hao Shan, Yizhe Fan, Yanhao Wu, Han Jiang, Yilong Ren, Zhenning Li, Haiyang Yu, Yanhu Shan, Rui Wu, Hao Zhao, Hongyang Li, Zhiyong Cui·· 1 days agoSignalEditorial score75

PlanContext: Benchmarking In-Context Driving Decision-Making

PlanContext: Benchmarking In-Context Driving Decision-Making

Summary

This paper introduces PlanContext, a benchmark for evaluating in-context driving decision-making. It constructs paired evaluations by varying high-level driving intentions while keeping the observed scene fixed to assess context-dependent decision-making. The study defines six representative driving contexts and evaluates contextual decision correctness, road compliance, and dynamic safety using class-balanced metrics. Experiments on 9,561 instances from NAVSIM and 23 public end-to-end driving models show that high conventional driving-quality scores do not consistently correspond to strong route-level in-context driving decision-making, highlighting the importance of this dimension for evaluating end-to-end driving systems.

Editorial context

The authors introduce PlanContext, a benchmark that evaluates in-context driving decision-making by varying high-level intentions while keeping scenes fixed, demonstrating that conventional trajectory quality scores do not consistently correlate with context-dependent decision correctness across 23 end-to-end models.

Source: arXiv Robotics — research abstracts · Read original article ↗

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Source:arXiv Robotics — research abstracts · arxiv.org

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