Dynamically Local-Enhancement Planner for Large-Scale Autonomous Driving

• URL: http://arxiv.org/abs/2502.21134v1
• Date: Fri, 28 Feb 2025 15:17:20 GMT
• Title: Dynamically Local-Enhancement Planner for Large-Scale Autonomous Driving
• Authors: Nanshan Deng, Weitao Zhou, Bo Zhang, Junze Wen, Kun Jiang, Zhong Cao, Diange Yang,
• Abstract summary: We introduce the concept of dynamically enhancing a basic driving planner with local driving data, without permanently modifying the planner itself.<n>Our approach introduces a position-varying Markov Decision Process formulation and a graph neural network that extracts region-specific driving features from local observation data.<n>The results show that our method outperforms the baseline policy in both safety (collision rate) and average reward, while maintaining a lighter scale.
• Score: 15.68766075910435
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Current autonomous vehicles operate primarily within limited regions, but there is increasing demand for broader applications. However, as models scale, their limited capacity becomes a significant challenge for adapting to novel scenarios. It is increasingly difficult to improve models for new situations using a single monolithic model. To address this issue, we introduce the concept of dynamically enhancing a basic driving planner with local driving data, without permanently modifying the planner itself. This approach, termed the Dynamically Local-Enhancement (DLE) Planner, aims to improve the scalability of autonomous driving systems without significantly expanding the planner's size. Our approach introduces a position-varying Markov Decision Process formulation coupled with a graph neural network that extracts region-specific driving features from local observation data. The learned features describe the local behavior of the surrounding objects, which is then leveraged to enhance a basic reinforcement learning-based policy. We evaluated our approach in multiple scenarios and compared it with a one-for-all driving model. The results show that our method outperforms the baseline policy in both safety (collision rate) and average reward, while maintaining a lighter scale. This approach has the potential to benefit large-scale autonomous vehicles without the need for largely expanding on-device driving models.

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