Automatic generation of optimal road trajectory for the rescue vehicle in case of emergency on mountain freeway using reinforcement learning approach

Yang, Jingwen; Yuan, Wenqiang; Ju, Yang; Han, Wanli; Zhao, Jianyou

doi:10.1049/itr2.12081

Cited by 11 publications

(8 citation statements)

References 31 publications

(63 reference statements)

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“…After the test report is generated, it can be saved as Excel or XML format. e data files generated during the test are automatically stored in the specified folder and can be opened and viewed [14].…”

Section: Related Workmentioning

confidence: 99%

Electronic Controller Automatic Test System Based on Intelligent Control Algorithm

Gong

2022

Journal of Electrical and Computer Engineering

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In order to improve the automatic test effect of the electronic controller, this paper combines the intelligent control algorithm to construct the automatic test system of the electronic controller. Moreover, the paper makes a detailed theoretical analysis of several basic problems relating to signal integrity and gives a general method to solve the main SI problems. In addition, this paper analyzes the principle of the microstrip transmission line parameter model and its relationship with the signal integrity and constructs an automated test system for an intelligent electronic controller. The simulation test results show that the electronic controller automated test system based on the intelligent control algorithm proposed in this paper can play an important role in the electronic controller automated test.

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Section: Related Workmentioning

confidence: 99%

Electronic Controller Automatic Test System Based on Intelligent Control Algorithm

Gong

2022

Journal of Electrical and Computer Engineering

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“…It can be applied to large road networks based on IoVs technique. Meanwhile, there still exist some limitations to be further studied: (1) How to design a route coordination mechanism that can produce Pareto optimization (shorter driving time and higher vehicle fairness) in mathematical; (2) Vehicular network simulation is expected to verify the validity of this work's contributions compared with the numerical experiments, which is too small and needs to be extended; (3) The definition of vehicular fairness coefficient can be improved by professional psychological research. To perfect the fairness mechanism-based CVRG theory, it is necessary to future study the fairness mechanism-based CVRG under various traffic scenarios.…”

Section: Discussionmentioning

confidence: 99%

“…Improving traffic management systems becomes a considerable challenge for city managers. Among the existing transportation technologies, vehicle route guidance (VRG) is regarded as one of the most effective methods to reduce traffic congestion by adjusting the traffic flow in space dimension [3].…”

Section: Introductionmentioning

confidence: 99%

A unified model for the fairness mechanism‐based coordinated vehicle route guidance

Zhang,

Khalgui,

et al. 2023

IET Intelligent Trans Sys

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Coordinated vehicle route guidance is recognized as an effective way to alleviate the Braess' paradox that new congestion is generated since numerous vehicles obey the same guidance from Google Maps. In conventional route games, decision‐makers are assumed to be completely rational (maximized utility is taken as an optimization objective). However, behavioral research finds that not only the expected profit is pursued, but also the fairness psychology is concerned by decision‐makers in game theory. In existing studies, the fairness concern of players is widely accepted in supply chains. Inspired by the fairness psychology of supply chain members, the vehicle fairness coefficient is originally proposed in this paper. Moreover, a unified fairness mechanism of the routing game is built to perfect the existing theoretical framework of route games. Specifically, the formulated vehicle fairness coefficient is incorporated into the payoff functions of a perfectly rational‐based routing game, which makes the single‐objective problem to be multiple. Compared with the current completely rational‐based route games, this improvement allows vehicles to focus not only on travel time but also on route schemes' fairness. The consideration of vehicle fairness concern leads the original symmetric payoff matrix of the route game to be asymmetric. Since the existing solving theorems are not necessarily valid for the asymmetrical multi‐player game, a matched reinforcement learning algorithm is adopted to solve this new game. An experimental study shows that the averaged fairness coefficient is increased from 0.798 to 1.00 on the basis of the equal travel time due to the working of the designed fairness mechanism.

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“…Ricardo et al [24] proposed a modified architecture that leverages pointer networks to better address the Orienteering problem with time windows related to dynamic time-dependent constraints. Yang et al [25] used DRL to automatically generate the road trajectory for the rescue vehicle in case of an emergency on the mountain freeway. A summary of this part of the study reveals that the existing studies did not consider the difference between the time window dimension and the travel time dimension.…”

Section: Application Of Drl To Vrp and Its Extension Problemsmentioning

confidence: 99%

“…Yang et al. [25] used DRL to automatically generate the road trajectory for the rescue vehicle in case of an emergency on the mountain freeway. A summary of this part of the study reveals that the existing studies did not consider the difference between the time window dimension and the travel time dimension.…”

Section: Introductionmentioning

confidence: 99%

Coordinated multi‐agent hierarchical deep reinforcement learning to solve multi‐trip vehicle routing problems with soft time windows

Zhang

Guan

2023

IET Intelligent Trans Sys

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Vehicle Routing Problem (VRP) is a widespread problem in the transportation field, which challenges the intelligent level of vehicle decisions. Multi‐Trip Vehicle Routing Problem with Time Windows (MTVRPTW), as a further evolved problem of VRP considering multiple departures from one depot and temporal constraint of visiting nodes, has developed into one of the critical issues in the scheduling of logistics, bus transit, railway, and aviation. Traditionally, MTVRPTW is solved by the heuristic algorithm, which is generally time‐consuming and of non‐steady results. Reinforcement learning (RL) and multi‐agent framework have become popular in solving VRP to get better performance. However, the lack of variant dimensions in searching space and knowledge exchange between agents inhibit the further improvement of algorithms. Therefore, a Coordinated Multi‐agent Hierarchical Deep Reinforcement Learning (CMA‐HDRL) method is proposed in this study to enhance the overall solution quality and convergence rate by constructing a three‐layered structure (time, communication, and global layers), which is particularly designed to handle the state space explosion and improve the collaboration between agents. The results show that the proposed method can significantly outperform the general genetic algorithm (GA), RL, multi‐agent algorithm, and hierarchical algorithm, not only from the effectiveness on the cost consisting of travel time and penalty time but also from the operation robustness.

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Automatic generation of optimal road trajectory for the rescue vehicle in case of emergency on mountain freeway using reinforcement learning approach

Cited by 11 publications

References 31 publications

Electronic Controller Automatic Test System Based on Intelligent Control Algorithm

Electronic Controller Automatic Test System Based on Intelligent Control Algorithm

A unified model for the fairness mechanism‐based coordinated vehicle route guidance

Coordinated multi‐agent hierarchical deep reinforcement learning to solve multi‐trip vehicle routing problems with soft time windows

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