Integrating ego-conditioned prediction and gap-driven motion planning for safe autonomous driving

Engineering notes

Experiments on the nuPlan dataset demonstrate that our approach significantly outperforms existing methods in planning stability, safety, and driving efficiency across multiple metrics.

Chinese explanation / 中文解读

中文解读待补充：本站会优先为端到端自动驾驶、BEV感知、3D目标检测、轨迹预测、路径规划、LiDAR感知等高价值论文补充中文说明。

Original abstract

Efficient and safe trajectory planning in complex traffic remains challenging due to weak prediction-planning coupling in existing methods. This paper proposes a coupled prediction-planning framework that deeply integrates intention-aware multi-agent prediction with gap-driven trajectory optimization. We first design an ego-conditioned interaction prediction module that samples candidate ego plans and uses a transformer-based network to predict surrounding vehicle responses, generating consistent scenario plans. A gap generation algorithm is then introduced, explicitly incorporating lane-change dynamics and interactive influences. We further develop a gap transition cost function to jointly evaluate candidate gap reachability and efficiency. Finally, a coarse trajectory generated by combining the Intelligent Driver Model (IDM) and Dynamic Programming (DP) is refined via a constrained Quadratic Programming (QP) module to satisfy continuity, smoothness, and safety constraints. Experiments on the nuPlan dataset demonstrate that our approach significantly outperforms existing methods in planning stability, safety, and driving efficiency across multiple metrics.

Links and sources

• Official / arXiv page

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