Real-time lateral obstacle avoidance method based on improved parabola tentacle algorithm and model prediction control

Dynamic obstacles in high-speed scenarios bring a challenge to the effectiveness and adaptation of active obstacle avoidance algorithm due to the time-varying characteristics. To realize safe collision avoidance in real time, a lateral path planning and tracking method is presented based on the parabola tentacle algorithm and Model Prediction Control (MPC) method. The merits of the proposed method lie in its ability to overcoming the lack of dynamics constraints in tentacle algorithm and avoiding path replanning facing the decoupling geometric and dynamic constraints. Firstly, the improved tentacles with parabola structure fan out with different curvatures discretizing the basic driving options of the vehicle. Additionally, a transition path from the tentacle path segment to road centerline is calculated by quintic polynomial to improve the smoothness of the reference path. To remedy the deficiency of the tentacle algorithm failing to ensure vehicle stability in high-speed scenarios, dynamics constraints are considered by the incorporation of MPC in the path execution level. The tentacle satisfying the safety and comfort constraints is executed by the MPC-based controller. Finally, the superiority of parabola tentacle is highlighted by the comparison with circle and clothoid tentacles. Furthermore, hardware-in-the-loop (HIL) experiments are carried out in two scenarios to verify the effectiveness of the proposed lateral obstacle avoidance algorithm. The results show that the proposed method can achieve safe and real-time dynamic obstacles avoidance as well as favorable path tracking accuracy.