Holistic view of the road transportation system based on real-time data sharing mechanism

• URL: http://arxiv.org/abs/2407.03187v2
• Date: Thu, 4 Jul 2024 03:47:50 GMT
• Title: Holistic view of the road transportation system based on real-time data sharing mechanism
• Authors: Li Tao, Dong Xiang, Hao Junfeng, Yin Ping, Xu Xiaoxue, Lai Maokai, Li Yuan, Peng Ting,
• Abstract summary: This paper constructs a space-time global view of the road traffic system based on a real-time sharing mechanism. It enables both road users and managers to timely access the driving intentions of nearby vehicles and the real-time status of road infrastructure.
• Score: 9.503118311645515
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Traditional manual driving and single-vehicle-based intelligent driving have limitations in real-time and accurate acquisition of the current driving status and intentions of surrounding vehicles, leading to vehicles typically maintaining appropriate safe distances from each other. Yet, accidents still frequently occur, especially in merging areas; meanwhile, it is difficult to comprehensively obtain the conditions of road infrastructure. These limitations not only restrict the further improvement of road capacity but also result in irreparable losses of life and property. To overcome this bottleneck, this paper constructs a space-time global view of the road traffic system based on a real-time sharing mechanism, enabling both road users and managers to timely access the driving intentions of nearby vehicles and the real-time status of road infrastructure.

Related papers

• GPT-Augmented Reinforcement Learning with Intelligent Control for Vehicle Dispatching [82.19172267487998]
  GARLIC: a framework of GPT-Augmented Reinforcement Learning with Intelligent Control for vehicle dispatching. This paper introduces GARLIC: a framework of GPT-Augmented Reinforcement Learning with Intelligent Control for vehicle dispatching.
  arXiv  Detail & Related papers  (2024-08-19T08:23:38Z)
• UAV-based Intelligent Information Systems on Winter Road Safety for Autonomous Vehicles [2.1985100893513834]
  A limited lane width can reduce the capacity of the road and raise the risk of serious accidents involving autonomous vehicles. In this research, a model that segments and estimates the width of the road from the perspectives of Uncrewed aerial vehicles and autonomous vehicles. The proposed approach is needed to empower self-driving cars with up-to-date and accurate insights, enhancing their adaptability and decision-making capabilities in winter landscapes.
  arXiv  Detail & Related papers  (2024-06-18T07:53:37Z)
• RACER: Epistemic Risk-Sensitive RL Enables Fast Driving with Fewer Crashes [57.319845580050924]
  We propose a reinforcement learning framework that combines risk-sensitive control with an adaptive action space curriculum. We show that our algorithm is capable of learning high-speed policies for a real-world off-road driving task.
  arXiv  Detail & Related papers  (2024-05-07T23:32:36Z)
• Automatic Extraction of Relevant Road Infrastructure using Connected vehicle data and Deep Learning Model [4.235459779667272]
  We propose a novel approach that leverages connected vehicle data and cutting-edge deep learning techniques. By employing geohashing to segment vehicle trajectories and then generating image representations of road segments, we utilize the YOLOv5 algorithm for accurate classification of both straight road segments and intersections. Experimental results demonstrate an impressive overall classification accuracy of 95%, with straight roads achieving a remarkable 97% F1 score and intersections reaching a 90% F1 score.
  arXiv  Detail & Related papers  (2023-08-10T15:57:47Z)
• DenseLight: Efficient Control for Large-scale Traffic Signals with Dense Feedback [109.84667902348498]
  Traffic Signal Control (TSC) aims to reduce the average travel time of vehicles in a road network. Most prior TSC methods leverage deep reinforcement learning to search for a control policy. We propose DenseLight, a novel RL-based TSC method that employs an unbiased reward function to provide dense feedback on policy effectiveness.
  arXiv  Detail & Related papers  (2023-06-13T05:58:57Z)
• Differentiable Control Barrier Functions for Vision-based End-to-End Autonomous Driving [100.57791628642624]
  We introduce a safety guaranteed learning framework for vision-based end-to-end autonomous driving. We design a learning system equipped with differentiable control barrier functions (dCBFs) that is trained end-to-end by gradient descent.
  arXiv  Detail & Related papers  (2022-03-04T16:14:33Z)
• 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)
• Neurocognitive and traffic based handover strategies [0.0]
  The goal is to combine neurocognitive measurement of the drivers state and the static and dynamic traffic related data to develop an interpretation of the current situation. This situation analysis should be the basis for the determination of the best takeover point.
  arXiv  Detail & Related papers  (2021-01-22T14:46:21Z)
• Emergent Road Rules In Multi-Agent Driving Environments [84.82583370858391]
  We analyze what ingredients in driving environments cause the emergence of road rules. We find that two crucial factors are noisy perception and agents' spatial density. Our results add empirical support for the social road rules that countries worldwide have agreed on for safe, efficient driving.
  arXiv  Detail & Related papers  (2020-11-21T09:43:50Z)
• Goal-Directed Occupancy Prediction for Lane-Following Actors [5.469556349325342]
  Predicting the possible future behaviors of vehicles that drive on shared roads is a crucial task for safe autonomous driving. We propose a new method that leverages the mapped road topology to reason over possible goals and predict the future spatial occupancy of dynamic road actors.
  arXiv  Detail & Related papers  (2020-09-06T20:44:59Z)

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.