Lane Departure Warning Estimation Using Yaw Acceleration


Em, Poh Ping and Hossen, Md. Jakir and Wong, Eng Kiong (2021) Lane Departure Warning Estimation Using Yaw Acceleration. Open Engineering, 11 (1). pp. 102-111. ISSN 2391-5439

[img] Text
Lane Departure Warning Estimation Using Yaw Acceleration.pdf
Restricted to Repository staff only

Download (22MB)


Lane departure collisions have contributed to the traffic accidents that cause millions of injuries and tens of thousands of casualties per year worldwide. Due to vision-based lane departure warning limitation from environmental conditions that affecting system performance, a model-based vehicle dynamics framework is proposed for estimating the lane departure event by using vehicle dynamics responses. The model-based vehicle dynamics framework mainly consists of a mathematical representation of 9-degree of freedom system, which permitted to pitch, roll, and yaw as well as to move in lateral and longitudinal directions with each tire allowed to rotate on its axle axis. The proposed model-based vehicle dynamics framework is created with a ride model, Calspan tire model, handling model, slip angle, and longitudinal slip subsystems. The vehicle speed and steering wheel angle datasets are used as the input in vehicle dynamics simulation for predicting lane departure event. Among the simulated vehicle dynamic responses, the yaw acceleration response is observed to provide earlier insight in predicting the future lane departure event compared to other vehicle dynamics responses. The proposed model-based vehicle dynamics framework had shown the effectiveness in estimating lane departure using steering wheel angle and vehicle speed inputs.

Item Type: Article
Uncontrolled Keywords: Lane departure warning estimation, Yaw acceleration, Model-based vehicle dynamics framework
Subjects: T Technology > TE Highway engineering. Roads and pavements > TE210-228.3 Construction details Including foundations, maintenance, equipment
Divisions: Faculty of Engineering and Technology (FET)
Depositing User: Ms Nurul Iqtiani Ahmad
Date Deposited: 19 Jan 2022 08:22
Last Modified: 19 Jan 2022 08:22


Downloads per month over past year

View ItemEdit (login required)