In this study, an obstacle avoidance controller based on nonlinear model predictive control is designed in autonomous vehicle navigation. The reference trajectory is predefined using a sigmoid function in accordance with road conditions. When obstacles suddenly appear on a predefined trajectory, the reference trajectory should be adjusted dynamically. For dynamic obstacles, a moving trend function is constructed to predict the obstacle position variances in the predictive horizon. Furthermore, a risk index is constructed and introduced into the cost function to realize collision avoidance by combining the relative position relationship between vehicle and obstacles in the predictive horizon. Meanwhile, lateral acceleration constraint is also considered to ensure vehicle stability. Finally, trajectory dynamic planning and tracking are integrated into a single-level model predictive controller. Simulation tests reveal that the designed controller can ensure real-time trajectory tracking and collision avoidance.

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Dynamic Trajectory Planning and Tracking for Autonomous Vehicle With Obstacle Avoidance Based on Model Predictive Control

Trajectory tracking control is a key technology in the research and development of autonomous vehicles. With the aim of addressing problems such as low control accuracy and poor real-time performance, which can occur easily when an autonomous vehicle avoids obstacles, this research focuses on the trajectory tracking control algorithm for autonomous vehicle considering cornering characteristics. First, the vehicle dynamics model and tire model are established through appropriate simplification. Then, based on the basic principle of model predictive control, a linear time-varying model predictive controller (LTV MPC) that considers the cornering characteristics is designed and optimized. Finally, using CarSim and MATLAB/Simulink software, a joint simulation model is established and the trajectory tracking performance of the controlled vehicle under different vehicle speeds and road adhesion conditions are tested through simulation experiments in combination with the double-shift line reference trajectory. The simulation results show the LTV MPC controller that considers cornering characteristics has good self-adaptability under complicated and severe working conditions, and no cases, such as car sideslip or track departure, were observed. Compared with other controllers and algorithms, the designed trajectory tracking controller has remarkable comprehensive performance, exhibits superior robustness and anti-interference ability, and significant improvements in the trajectory tracking control accuracy and real-time performance. The proposed control algorithm is of great importance in improving the tracking stability and driving safety of autonomous vehicles under complex extreme conditions and conducive to the further development and improvement of the technological level of intelligent vehicle driving assistance.

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Trajectory Tracking Control Algorithm for Autonomous Vehicle Considering Cornering Characteristics

Path following is the basic technology of the autonomous vehicle (AV), many preview control methods have been widely applied to path following tasks However, less of them take the variable vehicle velocities into account In fact, the velocity is an important factor affecting the tracking accuracy Especially, when an AV is in high velocity, it is not easy to achieve path following with high accuracy To improve the adaptivity of path following in different velocities, an improved adaptive path following control system (PFCS) constructed by a course angle optimal referential model (CAORM) and a model predictive controller (MPC) is developed in this paper The CAORM can provide the referential course angle, according to the vehicle longitudinal and lateral velocities, which significantly improves the adaptivity of the proposed PFCS in different velocities And the CAORM is mainly implemented by a fuzzy inference system and a novel preview model, using human driving experience The MPC is applied to realize the course control with high accuracy via manipulating the steering angle Finally, the tracking performance of the PFCS is verified via simulation experiments on the Simulink-CarSim platform

Improved Adaptive Path Following Control System for Autonomous Vehicle in Different Velocities

In this article, a novel approach of decision-making and motion control is designed for realizing safe and personalized driving of autonomous vehicles. A new lane-change intention generation model and a new lane-change decision-making algorithm are proposed. The feature of the proposed decision-making module is that the interactions between the ego vehicle and other surrounding vehicles are represented by the dynamic potential field (DPF) and embedded in the gap acceptance model to ensure the safety and personalization during driving. In addition, an integrated trajectory planning and tracking control algorithm, which incorporates the artificial potential field and constrained Delaunay triangulation (CDT) into the model predictive control framework, is developed. The newly developed integrated controller allows efficient execution of the expected motion. The proposed approach is tested under different driving conditions and further compared with an existing baseline method. The results show that the proposed approach is able to make safe and personalized decisions, and execute motion control more efficiently for automated driving under dynamic situations, validating its feasibility and effectiveness.

Toward Safe and Personalized Autonomous Driving: Decision-Making and Motion Control With DPF and CDT Techniques

This paper is concerned with the stability of the vehicular platoon consisting of a leader and multiple cooperative autonomous driving followers. The objective of the platoon control is to ensure all vehicles traveling at the same speed while maintaining a safety spacing. To achieve the objective, a novel control framework consisting of the distributed adaptive event-triggered observer and the car-following control protocol is proposed for the vehicular platoon control. The condition that only few following vehicles can access the information of the leader is considered. In order to design controllers while avoid using any global information, such as the system matrix and the state of the leader, a distributed event-triggered observer is proposed, such that each vehicle can observe the dynamics of the leader. Based on this observer, both collision avoidance and limited communication source of each vehicle are simultaneously considered in the design of the observer. It is shown that under the proposed control framework, the platoon can achieve asymptotical stable, meanwhile, the amount of transmission data and communication cost among vehicles can be reduced. Finally, numerical simulations are presented to show the applicability of the obtained results.

Distributed Adaptive Event-Triggered Control and Stability Analysis for Vehicular Platoon

Collision-free navigation of autonomous vehicles by means of convex quadratic programming (CQP) based model predictive control (MPC) is considered in this paper. A new collision-free navigation function is designed for real-time collision avoidance of an autonomous vehicle in both static and dynamic environments. Furthermore, vehicle shape is taken into consideration during trajectory generation as a convex polygonal region defined by linear constraints rather than a single point. Then, the MPC optimization problem with the vehicle shape is solved as a CQP-based MPC scheme in the sense of path planning. Compared to the previous MPC, which can only be reduced to a nonlinear programming problem, the control sequences of CQP-based MPC can be obtained quickly with improved real-time system performance. Simulations in diverse scenarios, including a real vehicle dataset, show the validity of the proposed approach.

Collision-Free Navigation of Autonomous Vehicles Using Convex Quadratic Programming-Based Model Predictive Control

Considering input constraints and parameter uncertainty of the nonholonomic wheeled mobile robots, a new control law has been designed in this paper to solve the tracking and stabilization control problems simultaneously The asymptotic convergence of the stabilization or tracking errors is achieved by a smooth controller with time-varying feedback parameters subject to set conditions A geometric-based parameter design strategy is employed to overcome the difficulties resulting from input constraints With the parameter design strategy, the designed parameters satisfy the set conditions and the control inputs stay in the constrained domain Experimental studies have been conducted to verify the effectiveness of the proposed control law

Gangbin Li

Papers

Collision-Free Navigation of Autonomous Vehicles Using Convex Quadratic Programming-Based Model Predictive Control

Simultaneous Stabilization and Tracking of Nonholonomic WMRs With Input Constraints: Controller Design and Experimental Validation