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The past decade has witnessed the rapid development of autonomous parking technology, since it has promising capacity to improve traffic efficiency and reduce the burden on drivers. However, it is prone to the trap of self-centeredness when each vehicle is automated parking in isolation. And it is easy to cause traffic congestion and even chaos when multiple autonomous vehicles require of parking into the same lot. In order to address the multiple vehicle parking problem, we propose a parking planning method with genetic algorithm. Firstly, an optimal mathematic model is established to describe the multiple autonomous vehicle parking problem. Secondly, a genetic algorithm is designed to solve the optimization problem. Thirdly, illustrative examples are developed to verify the parking planner. The performance of the present method indicates its competence in addressing parking multiple autonomous vehicles problem.

Although autonomous valet parking technology can replace the driver to complete the parking operation, it is easy to cause traffic chaos in the case of lacking scheduling for multiple parking agents, especially when multiple cars compete for the same parking slot at the same time. Therefore, in order to ensure orderly traffic and parking safety, it is necessary to allocate parking slots reasonably for multiple autonomous valet parking vehicles. The parking slots allocation model is built as an optimal problem with constraints. Both parking mileage cost and parking difficult cost are considering at the objective function in the optimization problem. There are three types of constraints. The first is the capacity limit of a single parking slot, the second is the space limit occupied by a single vehicle, and the third is the total capacity limit of the parking lot. After establishing parking slots allocation model, the immune algorithm is coded to solve the problem.

A tractor-trailer vehicle (TTV) consists of an actuated tractor attached with several full trailers. Because of its nonlinear and noncompleted constraints, it is a challenging task to avoid collisions for path planner. In this paper, we propose an efficient method to plan an optimal trajectory for TTV to reach the destination without any collision. To deal with the complicated constraints, the trajectory planning problem is formulated as an optimal control problem uniformly, which can be solved by the interior point method. A novel incremental optimization solving algorithm (IOSA) is proposed to accelerate the optimization process, which makes the number of trailers and the size of obstacles increase asynchronously. Simulation experiments are carried out in two scenarios with static obstacles. Compared with other methods, the results show that the planning method with IOSA outperforms in the efficiency.

This paper proposed a lane-change planning method for autonomous vehicle, aiming at fast obstacles avoidance in a way that make smooth and comfortable. The panning algorithm consists of dynamic programming and closed-loop forward simulation. The dynamic programming (DP) was employed to fast search a reference trajectory that avoids obstacles in topological configure space. And the closed-loop forward simulation (CFS) was used to track the reference trajectory for generating smooth trajectory, since the CFS being able to incorporate any nonlinear law and nonlinear vehicle constraints. Furthermore, an anti-windup lateral controller was designed to make the closed-loop forward simulation robust, as the controller being proved to be stable by Lyapunov function. Finally, the numerical results are provided to illustrate the effectiveness of the proposed method.

Tracking vehicle trajectories is essential for autonomous vehicles and advanced driver-assistance systems to understand traffic environment and evaluate collision risk. In order to reduce the position deviation and fluctuation of tracking on-road vehicle by millimeter-wave radar (MMWR), an interactive multi-model Kalman filter (IMM-KF) tracking algorithm including data association and track management is proposed. In general, it is difficult to model the target vehicle accurately due to lack of vehicle kinematics parameters, like wheel base, uncertainty of driving behavior and limitation of sensor’s field of view. To handle the uncertainty problem, an interacting multiple model (IMM) approach using Kalman filters is employed to estimate multitarget’s states. Then the compensation of radar ego motion is achieved, since the original measurement is under the radar polar coordinate system.

A real-time obstacle-avoidance trajectory planner for on-road autonomous vehicle is proposed in this paper. A cubic B spline core is parametric to generate path with continuous curvature as well as taking the extreme curvature limited by steer system into account. By sampling the target sets via offsetting along the reference path, lots path sets are produced with same heading. As embedded with collision checker and path evaluator, a path selector could pick out the best one to planning speed profile for coupling trajectory to track. Finally, according to the change of path curvature, the speed profile scheduler addresses the conflict of curvature and deceleration. Referencing to the ISO3888-2:2011, a collision avoidance scenario was design to validate the planner. The test results of ten cycles test illustrate that the planner has high enough real-time performance as mean plan time less than 100ms with success rate about 100%.