# Hierarchical End-to-End Autonomous Driving: Integrating BEV Perception with Deep Reinforcement Learning Source: [Hierarchical End-to-End Autonomous Driving: Integrating BEV Perception with Deep Reinforcement Learning]() Collection index: [llms.txt]() Data updated: 2025-10-26T03:23:18.913Z Authors: Siyi Lu, Lei He, Shengbo Eben Li, Yugong Luo, Jianqiang Wang, Keqiang Li Published: 2024-09-26T09:14:16.000Z ## Abstract End-to-end autonomous driving offers a streamlined alternative to the traditional modular pipeline, integrating perception, prediction, and planning within a single framework. While Deep Reinforcement Learning (DRL) has recently gained traction in this domain, existing approaches often overlook the critical connection between feature extraction of DRL and perception. In this paper, we bridge this gap by mapping the DRL feature extraction network directly to the perception phase, enabling clearer interpretation through semantic segmentation. By leveraging Bird's-Eye-View (BEV) representations, we propose a novel DRL-based end-to-end driving framework that utilizes multi-sensor inputs to construct a unified three-dimensional understanding of the environment. This BEV-based system extracts and translates critical environmental features into high-level abstract states for DRL, facilitating more informed control. Extensive experimental evaluations demonstrate that our approach not only enhances interpretability but also significantly outperforms state-of-the-art methods in autonomous driving control tasks, reducing the collision rate by 20%. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()