Shortest path establishment approaches for static and dynamic mobile nodes with quality of service

Sundar, Shyam; Basu, Aritra; Kar, Arunava; Kumar, R.; Kittur, Harish M.

doi:10.1504/eg.2018.089509

Cited by 1 publication

(1 citation statement)

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“…Path planning is to find a path from the starting point to the terminal point in a particular network according to some evaluation standards (such as the shortest or the fastest, etc) [9], which is an important way to reduce fuel consumption. Traditional path planning algorithm can only solve the static shortest path problem [10], due to not considering the real-time road conditions such as traffic jams, accidents, FIGURE 1: Overall framework of CACC etc., the shortest path is not the optimal path. Therefore, many scholars have carried out extensive research on the path planning problem [11], [12], and made some progress, but mainly focused on solving the shortest path or the shortest time, the research on energy saving path planning is very little.…”

Section: Introductionmentioning

confidence: 99%

An Intelligent Path Planning Scheme of Autonomous Vehicles Platoon Using Deep Reinforcement Learning on Network Edge

Chen

Jiang

et al. 2020

IEEE Access

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Recent advancements in Intelligent Transportation Systems suggest that the roads will gradually be filled with autonomous vehicles that are able to drive themselves while communicating with each other and the infrastructure. As a representative driving pattern of autonomous vehicles, the platooning technology has great potential for reducing transport costs by lowering fuel consumption and increasing traffic efficiency. In this paper, to improve the driving efficiency of autonomous vehicular platoon in terms of fuel consumption, a path planning scheme is envisioned using deep reinforcement learning on the network edge node. At first, the system model of autonomous vehicles platooning is given on the common highway. Next, a joint optimization problem is developed considering the task deadline and fuel consumption of each vehicle in the platoon. After that, a path determination strategy employing deep reinforcement learning is designed for the platoon. To make the readers readily follow, a case study is also presented with instantiated parameters. Numerical results shows that our proposed model could significantly reduce the fuel consumption of vehicle platoons while ensuring their task deadlines.

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