ITSC 2024 Paper Abstract

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Paper ThAT9.3

Tarhini, Fadel (University of Technology of Compiegne), TALJ, Reine (Université de Technologie de Compiègne, Heudiasyc), Doumiati, Moustapha (IREENA LAB UR4642)

Hybrid Energy-Efficient Local Path Planning for Autonomous Vehicles in Dynamic Environments

Scheduled for presentation during the Regular Session "Trajectory planning II" (ThAT9), Thursday, September 26, 2024, 11:10−11:30, Salon 17

2024 IEEE 27th International Conference on Intelligent Transportation Systems (ITSC), September 24- 27, 2024, Edmonton, Canada

This information is tentative and subject to change. Compiled on October 3, 2024

Keywords Automated Vehicle Operation, Motion Planning, Navigation, Electric Vehicles, Advanced Vehicle Safety Systems

Abstract

Efficient trajectory planning plays a crucial role in the development of autonomous vehicles, ensuring safe and optimized navigation in dynamic environments. This paper proposes a novel energy-efficient hybrid trajectory planning by integrating a sampling-based method with an optimization-based path refining method. It uses the strength of the sampling-based methods to reduce the solution space and generate a reactive trajectory in a dynamic environment. Following path selection, a septic path is generated and utilized as a reference for an energy-efficient path-refining optimization problem, producing a jerk-controlled trajectory with enhanced computational efficiency. The simulations were conducted in a joint-simulation environment using Simulink/Matlab and the Scaner Studio vehicle dynamics and driving environment simulator. The findings demonstrate the effectiveness of our approach in achieving significant energy savings while adeptly addressing dynamically changing environments.

 

 

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