Hierarchical End-to-End Autonomous Driving: Integrating BEV Perception with Deep Reinforcement Learning

• URL: http://arxiv.org/abs/2409.17659v1
• Date: Thu, 26 Sep 2024 09:14:16 GMT
• Title: Hierarchical End-to-End Autonomous Driving: Integrating BEV Perception with Deep Reinforcement Learning
• Authors: Siyi Lu, Lei He, Shengbo Eben Li, Yugong Luo, Jianqiang Wang, Keqiang Li,
• Abstract summary: End-to-end autonomous driving offers a streamlined alternative to the traditional modular pipeline. Deep Reinforcement Learning (DRL) has recently gained traction in this domain. We bridge this gap by mapping the DRL feature extraction network directly to the perception phase.
• Score: 23.21761407287525
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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%.

Related papers

• Learning Content-Aware Multi-Modal Joint Input Pruning via Bird's-Eye-View Representation [11.074747442071729]
  We introduce a novel content-aware multi-modal joint input pruning technique. We validatethe efficacy of our approach through extensive experiments on the NuScenes dataset.
  arXiv  Detail & Related papers  (2024-10-09T03:30:00Z)
• DiFSD: Ego-Centric Fully Sparse Paradigm with Uncertainty Denoising and Iterative Refinement for Efficient End-to-End Self-Driving [55.53171248839489]
  We propose an ego-centric fully sparse paradigm, named DiFSD, for end-to-end self-driving. Specifically, DiFSD mainly consists of sparse perception, hierarchical interaction and iterative motion planner. Experiments conducted on nuScenes and Bench2Drive datasets demonstrate the superior planning performance and great efficiency of DiFSD.
  arXiv  Detail & Related papers  (2024-09-15T15:55:24Z)
• An Examination of Offline-Trained Encoders in Vision-Based Deep Reinforcement Learning for Autonomous Driving [0.0]
  Research investigates the challenges Deep Reinforcement Learning (DRL) faces in Partially Observable Markov Decision Processes (POMDP) Our research adopts an offline-trained encoder to leverage large video datasets through self-supervised learning to learn generalizable representations. We show that the features learned by watching BDD100K driving videos can be directly transferred to achieve lane following and collision avoidance in CARLA simulator.
  arXiv  Detail & Related papers  (2024-09-02T14:16:23Z)
• Semantic Communication for Cooperative Perception using HARQ [51.148203799109304]
  We leverage an importance map to distill critical semantic information, introducing a cooperative perception semantic communication framework. To counter the challenges posed by time-varying multipath fading, our approach incorporates the use of frequency-division multiplexing (OFDM) along with channel estimation and equalization strategies. We introduce a novel semantic error detection method that is integrated with our semantic communication framework in the spirit of hybrid automatic repeated request (HARQ)
  arXiv  Detail & Related papers  (2024-08-29T08:53:26Z)
• Hierarchical and Decoupled BEV Perception Learning Framework for Autonomous Driving [52.808273563372126]
  This paper proposes a novel hierarchical BEV perception paradigm, aiming to provide a library of fundamental perception modules and user-friendly graphical interface. We conduct the Pretrain-Finetune strategy to effectively utilize large scale public datasets and streamline development processes. We also present a Multi-Module Learning (MML) approach, enhancing performance through synergistic and iterative training of multiple models.
  arXiv  Detail & Related papers  (2024-07-17T11:17:20Z)
• Reason2Drive: Towards Interpretable and Chain-based Reasoning for Autonomous Driving [38.28159034562901]
  Reason2Drive is a benchmark dataset with over 600K video-text pairs. We characterize the autonomous driving process as a sequential combination of perception, prediction, and reasoning steps. We introduce a novel aggregated evaluation metric to assess chain-based reasoning performance in autonomous systems.
  arXiv  Detail & Related papers  (2023-12-06T18:32:33Z)
• Interactive Autonomous Navigation with Internal State Inference and Interactivity Estimation [58.21683603243387]
  We propose three auxiliary tasks with relational-temporal reasoning and integrate them into the standard Deep Learning framework. These auxiliary tasks provide additional supervision signals to infer the behavior patterns other interactive agents. Our approach achieves robust and state-of-the-art performance in terms of standard evaluation metrics.
  arXiv  Detail & Related papers  (2023-11-27T18:57:42Z)
• DeepIPC: Deeply Integrated Perception and Control for an Autonomous Vehicle in Real Environments [7.642646077340124]
  We introduce DeepIPC, a novel end-to-end model tailored for autonomous driving. DeepIPC seamlessly integrates perception and control tasks. Our evaluation demonstrates DeepIPC's superior performance in terms of drivability and multi-task efficiency.
  arXiv  Detail & Related papers  (2022-07-20T14:20:35Z)
• Federated Deep Learning Meets Autonomous Vehicle Perception: Design and Verification [168.67190934250868]
  Federated learning empowered connected autonomous vehicle (FLCAV) has been proposed. FLCAV preserves privacy while reducing communication and annotation costs. It is challenging to determine the network resources and road sensor poses for multi-stage training.
  arXiv  Detail & Related papers  (2022-06-03T23:55:45Z)
• Vehicular Cooperative Perception Through Action Branching and Federated Reinforcement Learning [101.64598586454571]
  A novel framework is proposed to allow reinforcement learning-based vehicular association, resource block (RB) allocation, and content selection of cooperative perception messages (CPMs) A federated RL approach is introduced in order to speed up the training process across vehicles. Results show that federated RL improves the training process, where better policies can be achieved within the same amount of time compared to the non-federated approach.
  arXiv  Detail & Related papers  (2020-12-07T02:09:15Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.