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arXiv · 2611.03067

PlanContext: Benchmarking In-Context Driving Decision-Making

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
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Abstract

Real-world driving requires context-aware decision-making. Existing end-to-end driving benchmarks primarily assess trajectory quality through safety, compliance, comfort, and driving progress, leaving context-dependent decision-making insufficiently characterized. To fill this gap, we present PlanContext, a benchmark for evaluating in-context driving decision-making. PlanContext constructs paired evaluations by varying high-level driving intentions while keeping the observed scene fixed, enabling a natural and controllable assessment of context-dependent decision-making. We further define six representative driving contexts and jointly evaluate contextual decision correctness, road compliance, and dynamic safety with class-balanced metrics. Experiments on 9,561 scene-condition 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. Further analysis shows that model behavior varies with decision timing, road structure, and traffic interaction depending on the decision required by the current context. Our results highlight in-context driving decision-making as an important complementary dimension for evaluating end-to-end driving systems.

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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. 2026-11-02. PlanContext: Benchmarking In-Context Driving Decision-Making. https://arxiv.org/abs/2611.03067

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