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.

Related papers

• Diffusion-Based Planning for Autonomous Driving with Flexible Guidance [19.204115959760788]
  We propose a novel transformer-based Diffusion Planner for closed-loop planning.<n>Our model supports joint modeling of both prediction and planning tasks.<n>It achieves state-of-the-art closed-loop performance with robust transferability in diverse driving styles.
  arXiv  Detail & Related papers  (2025-01-26T15:49:50Z)
• AdaWM: Adaptive World Model based Planning for Autonomous Driving [34.57859869929471]
  World model based reinforcement learning (RL) has emerged as a promising approach for autonomous driving. The pretrain-finetune paradigm is often used, where online RL is performance by a pretrained model and a policy learned offline. We introduce AdaWM, an Adaptive World Model based planning method, featuring two key steps: (a) mismatch identification, which quantifies the mismatches and informs the finetuning strategy, and (b) alignment-driven finetuning, which selectively updates either the policy or the model as needed.
  arXiv  Detail & Related papers  (2025-01-22T18:34:51Z)
• A Survey of World Models for Autonomous Driving [63.33363128964687]
  Recent breakthroughs in autonomous driving have been propelled by advances in robust world modeling.<n>This paper systematically reviews recent advances in world models for autonomous driving.
  arXiv  Detail & Related papers  (2025-01-20T04:00:02Z)
• DrivingGPT: Unifying Driving World Modeling and Planning with Multi-modal Autoregressive Transformers [61.92571851411509]
  We introduce a multimodal driving language based on interleaved image and action tokens, and develop DrivingGPT to learn joint world modeling and planning.<n>Our DrivingGPT demonstrates strong performance in both action-conditioned video generation and end-to-end planning, outperforming strong baselines on large-scale nuPlan and NAVSIM benchmarks.
  arXiv  Detail & Related papers  (2024-12-24T18:59:37Z)
• End-to-end Driving in High-Interaction Traffic Scenarios with Reinforcement Learning [24.578178308010912]
  We propose an end-to-end model-based RL algorithm named Ramble to address these issues. By learning a dynamics model of the environment, Ramble can foresee upcoming traffic events and make more informed, strategic decisions. Ramble achieves state-of-the-art performance regarding route completion rate and driving score on the CARLA Leaderboard 2.0, showcasing its effectiveness in managing complex and dynamic traffic situations.
  arXiv  Detail & Related papers  (2024-10-03T06:45:59Z)
• Parameterized Decision-making with Multi-modal Perception for Autonomous Driving [12.21578713219778]
  We propose a parameterized decision-making framework with multi-modal perception based on deep reinforcement learning, called AUTO. A hybrid reward function takes into account aspects of safety, traffic efficiency, passenger comfort, and impact to guide the framework to generate optimal actions.
  arXiv  Detail & Related papers  (2023-12-19T08:27:02Z)
• Interpretable and Flexible Target-Conditioned Neural Planners For Autonomous Vehicles [22.396215670672852]
  Prior work only learns to estimate a single planning trajectory, while there may be multiple acceptable plans in real-world scenarios. We propose an interpretable neural planner to regress a heatmap, which effectively represents multiple potential goals in the bird's-eye view of an autonomous vehicle. Our systematic evaluation on the Lyft Open dataset shows that our model achieves a safer and more flexible driving performance than prior works.
  arXiv  Detail & Related papers  (2023-09-23T22:13:03Z)
• Dual policy as self-model for planning [71.73710074424511]
  We refer to the model used to simulate one's decisions as the agent's self-model. Inspired by current reinforcement learning approaches and neuroscience, we explore the benefits and limitations of using a distilled policy network as the self-model.
  arXiv  Detail & Related papers  (2023-06-07T13:58:45Z)
• Decentralized Vehicle Coordination: The Berkeley DeepDrive Drone Dataset and Consensus-Based Models [76.32775745488073]
  We present a novel dataset and modeling framework designed to study motion planning in understructured environments. We demonstrate that a consensus-based modeling approach can effectively explain the emergence of priority orders observed in our dataset.
  arXiv  Detail & Related papers  (2022-09-19T05:06:57Z)
• Tackling Real-World Autonomous Driving using Deep Reinforcement Learning [63.3756530844707]
  In this work, we propose a model-free Deep Reinforcement Learning Planner training a neural network that predicts acceleration and steering angle. In order to deploy the system on board the real self-driving car, we also develop a module represented by a tiny neural network.
  arXiv  Detail & Related papers  (2022-07-05T16:33:20Z)
• Generating Useful Accident-Prone Driving Scenarios via a Learned Traffic Prior [135.78858513845233]
  STRIVE is a method to automatically generate challenging scenarios that cause a given planner to produce undesirable behavior, like collisions. To maintain scenario plausibility, the key idea is to leverage a learned model of traffic motion in the form of a graph-based conditional VAE. A subsequent optimization is used to find a "solution" to the scenario, ensuring it is useful to improve the given planner.
  arXiv  Detail & Related papers  (2021-12-09T18:03:27Z)
• Learning to drive from a world on rails [78.28647825246472]
  We learn an interactive vision-based driving policy from pre-recorded driving logs via a model-based approach. A forward model of the world supervises a driving policy that predicts the outcome of any potential driving trajectory. Our method ranks first on the CARLA leaderboard, attaining a 25% higher driving score while using 40 times less data.
  arXiv  Detail & Related papers  (2021-05-03T05:55:30Z)

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.