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Steering Control Based on the Yaw Rate and Projected Steering Wheel Angle in Evasion Maneuvers
ISSN: 0148-7191, e-ISSN: 2688-3627
Published April 3, 2018 by SAE International in United States
This content contains downloadable datasetsAnnotation ability available
When automobiles are at the threat of collisions, steering usually needs shorter longitudinal distance than braking for collision avoidance, especially under the condition of high speed or low adhesion. Thus, more collision accidents can be avoided in the same situation. The steering assistance is in need since the operation is hard for drivers. And considering the dynamic characteristics of vehicles in those maneuvers, the real-time and the accuracy of the assisted algorithms is essential.
In view of the above problems, this paper first takes lateral acceleration of the vehicle as the constraint, aiming at the collision avoidance situation of the straight lane and the stable driving inside the curve, and trajectory of the collision avoidance is derived by a quintic polynomial. Based on the control of the steering wheel angle by the optimal preview control algorithm, the differential braking control is carried out by using the feedbacks of yaw rate and the projected steering wheel angle information to improve the accuracy of trajectory tracking and the stability of the ego vehicle in evasion maneuver.
Simulation analysis based on the vehicle dynamic software (ASM) is conducted in typical maneuvers. And the results show that the coordinated steering algorithm can further improve vehicle tracking accuracy and vehicles’ stability when using the same collision avoidance trajectory under the limit of designed lateral acceleration. It can partly decrease the influence of error of steering systems since the use of projected steering wheel angle and contribute to the convergence of lateral accelerations.
CitationYe, Y., Zhao, J., Wu, J., Zhu, B. et al., "Steering Control Based on the Yaw Rate and Projected Steering Wheel Angle in Evasion Maneuvers," SAE Technical Paper 2018-01-0030, 2018, https://doi.org/10.4271/2018-01-0030.
Data Sets - Support Documents
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