An End-to-end Deep Reinforcement Learning Approach for the Long-term Short-term Planning on the Frenet Space

• URL: http://arxiv.org/abs/2011.13098v1
• Date: Thu, 26 Nov 2020 02:40:07 GMT
• Title: An End-to-end Deep Reinforcement Learning Approach for the Long-term Short-term Planning on the Frenet Space
• Authors: Majid Moghadam, Ali Alizadeh, Engin Tekin and Gabriel Hugh Elkaim
• Abstract summary: This paper presents a novel end-to-end continuous deep reinforcement learning approach towards autonomous cars' decision-making and motion planning. For the first time, we define both states and action spaces on the Frenet space to make the driving behavior less variant to the road curvatures. The algorithm generates continuoustemporal trajectories on the Frenet frame for the feedback controller to track.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Tactical decision making and strategic motion planning for autonomous highway driving are challenging due to the complication of predicting other road users' behaviors, diversity of environments, and complexity of the traffic interactions. This paper presents a novel end-to-end continuous deep reinforcement learning approach towards autonomous cars' decision-making and motion planning. For the first time, we define both states and action spaces on the Frenet space to make the driving behavior less variant to the road curvatures than the surrounding actors' dynamics and traffic interactions. The agent receives time-series data of past trajectories of the surrounding vehicles and applies convolutional neural networks along the time channels to extract features in the backbone. The algorithm generates continuous spatiotemporal trajectories on the Frenet frame for the feedback controller to track. Extensive high-fidelity highway simulations on CARLA show the superiority of the presented approach compared with commonly used baselines and discrete reinforcement learning on various traffic scenarios. Furthermore, the proposed method's advantage is confirmed with a more comprehensive performance evaluation against 1000 randomly generated test scenarios.

Related papers

• 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)
• DiFSD: Ego-Centric Fully Sparse Paradigm with Uncertainty Denoising and Iterative Refinement for Efficient End-to-End Autonomous 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 dataset demonstrate the superior planning performance and great efficiency of DiFSD.
  arXiv  Detail & Related papers  (2024-09-15T15:55:24Z)
• Eco-Driving Control of Connected and Automated Vehicles using Neural Network based Rollout [0.0]
  Connected and autonomous vehicles have the potential to minimize energy consumption. Existing deterministic and methods created to solve the eco-driving problem generally suffer from high computational and memory requirements. This work proposes a hierarchical multi-horizon optimization framework implemented via a neural network.
  arXiv  Detail & Related papers  (2023-10-16T23:13:51Z)
• PDFormer: Propagation Delay-Aware Dynamic Long-Range Transformer for Traffic Flow Prediction [78.05103666987655]
  spatial-temporal Graph Neural Network (GNN) models have emerged as one of the most promising methods to solve this problem. We propose a novel propagation delay-aware dynamic long-range transFormer, namely PDFormer, for accurate traffic flow prediction. Our method can not only achieve state-of-the-art performance but also exhibit competitive computational efficiency.
  arXiv  Detail & Related papers  (2023-01-19T08:42:40Z)
• RLPG: Reinforcement Learning Approach for Dynamic Intra-Platoon Gap Adaptation for Highway On-Ramp Merging [14.540226579203207]
  A platoon refers to a group of vehicles traveling together in very close proximity. Recent research has revealed a detrimental effect of the extremely small intra-platoon gap on traffic flow for highway on-ramp merging. We present a novel reinforcement learning framework that adaptively adjusts the intra-platoon gap of an individual platoon member to maximize traffic flow.
  arXiv  Detail & Related papers  (2022-12-07T07:33:54Z)
• 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)
• GINK: Graph-based Interaction-aware Kinodynamic Planning via Reinforcement Learning for Autonomous Driving [10.782043595405831]
  There are many challenges in applying deep reinforcement learning (D) to autonomous driving in a structured environment such as an urban area. In this paper, we suggest a new framework that effectively combines graph-based intention representation and reinforcement learning for dynamic planning. The experiments show the state-of-the-art performance of our approach compared to the existing baselines.
  arXiv  Detail & Related papers  (2022-06-03T10:37:25Z)
• End-to-End Intersection Handling using Multi-Agent Deep Reinforcement Learning [63.56464608571663]
  Navigating through intersections is one of the main challenging tasks for an autonomous vehicle. In this work, we focus on the implementation of a system able to navigate through intersections where only traffic signs are provided. We propose a multi-agent system using a continuous, model-free Deep Reinforcement Learning algorithm used to train a neural network for predicting both the acceleration and the steering angle at each time step.
  arXiv  Detail & Related papers  (2021-04-28T07:54:40Z)
• Planning on the fast lane: Learning to interact using attention mechanisms in path integral inverse reinforcement learning [20.435909887810165]
  General-purpose trajectory planning algorithms for automated driving utilize complex reward functions. Deep learning approaches have been successfully applied to predict local situation-dependent reward functions. We present a neural network architecture that uses a policy attention mechanism to generate a low-dimensional context vector.
  arXiv  Detail & Related papers  (2020-07-11T15:25:44Z)
• Constructing Geographic and Long-term Temporal Graph for Traffic Forecasting [88.5550074808201]
  We propose Geographic and Long term Temporal Graph Convolutional Recurrent Neural Network (GLT-GCRNN) for traffic forecasting. In this work, we propose a novel framework for traffic forecasting that learns the rich interactions between roads sharing similar geographic or longterm temporal patterns.
  arXiv  Detail & Related papers  (2020-04-23T03:50:46Z)
• Spatial-Temporal Transformer Networks for Traffic Flow Forecasting [74.76852538940746]
  We propose a novel paradigm of Spatial-Temporal Transformer Networks (STTNs) to improve the accuracy of long-term traffic forecasting. Specifically, we present a new variant of graph neural networks, named spatial transformer, by dynamically modeling directed spatial dependencies. The proposed model enables fast and scalable training over a long range spatial-temporal dependencies.
  arXiv  Detail & Related papers  (2020-01-09T10:21:04Z)

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.