Modeling Adaptive Platoon and Reservation Based Autonomous Intersection Control: A Deep Reinforcement Learning Approach

• URL: http://arxiv.org/abs/2206.12419v1
• Date: Fri, 24 Jun 2022 08:50:36 GMT
• Title: Modeling Adaptive Platoon and Reservation Based Autonomous Intersection Control: A Deep Reinforcement Learning Approach
• Authors: Duowei Li (1 and 2), Jianping Wu (1), Feng Zhu (2), Tianyi Chen (2), and Yiik Diew Wong (2) ((1) Department of Civil Engineering, Tsinghua University, China, (2) School of Civil and Environmental Engineering, Nanyang Technological University, Singapore)
• Abstract summary: This study proposes an adaptive platoon based autonomous intersection control model powered by deep reinforcement learning (DRL) technique. When tested on a traffic micro-simulator, our proposed model exhibits superior performances on travel efficiency and fuel conservation as compared to the state-of-the-art methods.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: As a strategy to reduce travel delay and enhance energy efficiency, platooning of connected and autonomous vehicles (CAVs) at non-signalized intersections has become increasingly popular in academia. However, few studies have attempted to model the relation between the optimal platoon size and the traffic conditions around the intersection. To this end, this study proposes an adaptive platoon based autonomous intersection control model powered by deep reinforcement learning (DRL) technique. The model framework has following two levels: the first level adopts a First Come First Serve (FCFS) reservation based policy integrated with a nonconflicting lane selection mechanism to determine vehicles' passing priority; and the second level applies a deep Q-network algorithm to identify the optimal platoon size based on the real-time traffic condition of an intersection. When being tested on a traffic micro-simulator, our proposed model exhibits superior performances on travel efficiency and fuel conservation as compared to the state-of-the-art methods.

Related papers

• 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)
• MetaFollower: Adaptable Personalized Autonomous Car Following [63.90050686330677]
  We propose an adaptable personalized car-following framework - MetaFollower. We first utilize Model-Agnostic Meta-Learning (MAML) to extract common driving knowledge from various CF events. We additionally combine Long Short-Term Memory (LSTM) and Intelligent Driver Model (IDM) to reflect temporal heterogeneity with high interpretability.
  arXiv  Detail & Related papers  (2024-06-23T15:30:40Z)
• Integrating Higher-Order Dynamics and Roadway-Compliance into Constrained ILQR-based Trajectory Planning for Autonomous Vehicles [3.200238632208686]
  Trajectory planning aims to produce a globally optimal route for Autonomous Passenger Vehicles. Existing implementations utilizing the vehicle bicycle kinematic model may not guarantee controllable trajectories. We augment this model by higher-order terms, including the first and second-order derivatives of curvature and longitudinal jerk.
  arXiv  Detail & Related papers  (2023-09-25T22:30:18Z)
• Communication Resources Constrained Hierarchical Federated Learning for End-to-End Autonomous Driving [67.78611905156808]
  This paper proposes an optimization-based Communication Resource Constrained Hierarchical Federated Learning framework. Results show that the proposed CRCHFL both accelerates the convergence rate and enhances the generalization of federated learning autonomous driving model.
  arXiv  Detail & Related papers  (2023-06-28T12:44:59Z)
• Unified Automatic Control of Vehicular Systems with Reinforcement Learning [64.63619662693068]
  This article contributes a streamlined methodology for vehicular microsimulation. It discovers high performance control strategies with minimal manual design. The study reveals numerous emergent behaviors resembling wave mitigation, traffic signaling, and ramp metering.
  arXiv  Detail & Related papers  (2022-07-30T16:23:45Z)
• COOR-PLT: A hierarchical control model for coordinating adaptive platoons of connected and autonomous vehicles at signal-free intersections based on deep reinforcement learning [0.0]
  This study proposes a hierarchical control model, named COOR-PLT, to coordinate adaptive CAV platoons at a signal-free intersection. The model is validated and examined on the simulator of Urban Mobility (SUMO)
  arXiv  Detail & Related papers  (2022-07-01T02:22:31Z)
• Eco-driving for Electric Connected Vehicles at Signalized Intersections: A Parameterized Reinforcement Learning approach [6.475252042082737]
  This paper proposes an eco-driving framework for electric connected vehicles (CVs) based on reinforcement learning (RL) We show that our strategy can significantly reduce energy consumption by learning proper action schemes without any interruption of other human-driven vehicles (HDVs)
  arXiv  Detail & Related papers  (2022-06-24T04:11:28Z)
• AI-aided Traffic Control Scheme for M2M Communications in the Internet of Vehicles [61.21359293642559]
  The dynamics of traffic and the heterogeneous requirements of different IoV applications are not considered in most existing studies. We consider a hybrid traffic control scheme and use proximal policy optimization (PPO) method to tackle it.
  arXiv  Detail & Related papers  (2022-03-05T10:54:05Z)
• 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)
• Decision-making at Unsignalized Intersection for Autonomous Vehicles: Left-turn Maneuver with Deep Reinforcement Learning [17.715274169051494]
  This work proposes a deep reinforcement learning based left-turn decision-making framework at unsignalized intersection for autonomous vehicles. The presented decision-making strategy could efficaciously reduce the collision rate and improve transport efficiency. This work also reveals that the constructed left-turn control structure has a great potential to be applied in real-time.
  arXiv  Detail & Related papers  (2020-08-14T22:44:26Z)
• Learning Scalable Multi-Agent Coordination by Spatial Differentiation for Traffic Signal Control [8.380832628205372]
  We design a multiagent coordination framework based on Deep Reinforcement Learning methods for traffic signal control. Specifically, we propose the Spatial Differentiation method for coordination which uses the temporal-spatial information in the replay buffer to amend the reward of each action.
  arXiv  Detail & Related papers  (2020-02-27T02:16:00Z)

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.