Research on Trajectory Prediction Based on Front Vehicle Sideslip Recognition
Year: 2025
Type: article
Abstract: In order to solve the problem of emergency collision avoidance of autonomous vehicles when the front vehicle is unstable and sliding under high-speed conditions, a research method for the state recognition of the front side-skid vehicle and the trajectory prediction of the front side-skid vehicle was proposed. By extracting the key features of the vehicle in front of the vehicle in danger of sliding to build a skidding recognition model of the vehicle in front, a skidding recognition strategy of the vehicle in front was designed based on the extracted skidding feature indexes to judge the skidding state of the vehicle in front. The state quantity of the sliding vehicle in front is selected, and the constant rotation rate and acceleration model (CTRA) is established to predict the trajectory of the sliding vehicle in front in a short time. Considering the simplified assumptions of the model and the noise in the process of sensor perception information, the Unscented Kalman Filter (UKF) is used to deal with the uncertainty in the trajectory prediction process, the possible position and covariance of the front sideslipping vehicle are calculated, and the possible future area of the front sideslipping vehicle is estimated under the condition of a probability of 0.9. Through the established Carsim and Simulink co-simulation platform, the effectiveness of the front vehicle skidding state recognition strategy and the accuracy of the trajectory prediction of the sliding vehicle are verified under the condition of high speed and low attachment.
Source: World Electric Vehicle Journal
DOI: 10.3390/wevj16040241
Authors: Jian Ou, Xiaolong Cheng, Pengju Zhang
Institution: Chongqing University of Technology
Language: English
Subfield: Automotive Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $1,689
OpenAlex ID: W4409635877