Tracking and Safety Control for Car-Trailer System with Optimization

DOI

10.2991/assehr.k.220504.041How to use a DOI?

Keywords

Dynamic function; Energy function; Optimization; Genetic algorithm; Sequential quadratic programming method

Abstract

This Study aims to develop an automatic car-trailer system tracking and safety control model and optimize every parameter related to improve traffic safety. The authors proposed a car-trailer system tracking and safe control model. Kinetics, dynamics, and simulation code are established for both tractor and trailer. Moreover, a safety control is introduced to avoid any obstacle in the path. Furthermore, by using ISIGHT, algorithms (Genetic Algorithms and Successive quadratic programming (SQP)) are used to optimize parameters used in the car-trailer system. Thus, shortest traveling time and smallest risk of rolling-over for trailer are ensured. The numerical deduction for kinetic, dynamics, safety control are successfully established. MATLAB simulations are programmed correctly to imitate the moving path of both tractor and trailer. Optimization are finished smoothly with clear result. Originality/value: this is a simulation of tractor-trailer model and accomplishment of its safe-driving and path-optimizing features. there are two main contributions: the first is the simulation of tractor-trailer model and the accomplishment of its tracking and collision avoidance functions; the second is investigating trajectory with the shortest time traveled through optimization. this paper provides a reference to develop self-driving car-trailer with optimized attributes and path choosing.

Copyright

© 2022 The Authors. Published by Atlantis Press SARL.

Open Access

This is an open access article distributed under the CC BY-NC 4.0 license.

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TY  - CONF
AU  - Boshen Yang
AU  - Tianyi Zhang
PY  - 2022
DA  - 2022/06/01
TI  - Tracking and Safety Control for Car-Trailer System with Optimization
BT  - Proceedings of the 2022 8th International Conference on Humanities and Social Science Research (ICHSSR 2022)
PB  - Atlantis Press
SP  - 223
EP  - 228
SN  - 2352-5398
UR  - https://doi.org/10.2991/assehr.k.220504.041
DO  - 10.2991/assehr.k.220504.041
ID  - Yang2022
ER  -