Open AccessOptimal vehicle trajectory planning scheme considering passengers’ comfort for advanced driving assistance
Author(s) -
Wei Gao,
Haiqing Liu,
Kumin Teng,
Laxmisha Rai
Publication year - 2021
Publication title -
iop conference series. earth and environmental science
Language(s) - English
Resource type - Journals
eISSN - 1755-1307
pISSN - 1755-1315
DOI - 10.1088/1755-1315/784/1/012010
Subject(s) - intersection (aeronautics) , acceleration , trajectory , speed limit , control theory (sociology) , computer science , interval (graph theory) , limit (mathematics) , discretization , genetic algorithm , mathematical optimization , simulation , engineering , mathematics , artificial intelligence , mathematical analysis , physics , civil engineering , control (management) , classical mechanics , combinatorics , astronomy , aerospace engineering
Speed guidance is a significant application in the driver assistance system or driverless automated system. Considering passengers’ comfort under different road conditions, this paper proposes an optimal vehicle speed trajectory planning strategy using the nonlinear programming solution method. In this method, the Pearl curve is taken as the basic model to describe the time-interval characteristics so that the speed and acceleration variation can be further obtained. Taking the weighted dimensionless average accelerated speed and vehicle travelling time as optimal objective, and the speed limit of intersection, speed limit of section, the position of the intersection, the maximum acceleration and deceleration rates, and the arrival time at the downstream intersection as constraints, a multi-variable and single-objective optimization model is built and calculated using Genetic Algorithm after discretization process of the original continuous acceleration variation curve. Results from case study shows that the proposed method presents substantial performance in planning a vehicle trajectory with optimal comfort on the basis that the vehicle arrives the downstream intersection as early as possible.