Autonomous parking is one significant autonomous application and will be implemented in daily life in the near future. Due to encountered narrow environments, the issues related to autonomous parking, such as path quality requirements, strict collision avoidance, and motion direction changes, must be overcome properly. Moreover, to be applied in daily driving activities, real‐time planning and human preference should be fulfilled by the designed motion planners. Therefore, an efficient and human‐like motion planning method based on the revised Bidirectional Rapidly‐Exploring Random Tree* (Bi‐RRT*) with Reeds‐Shepp curve is presented. The proposed method results in human‐like paths which have high trajectory quality and consistency for parking scenarios due to the revised Bi‐RRT* framework. Strict collision checking model guarantees the resulting paths to be collision‐free and even leaves safe distance from obstacles and uncertainties. State space adjustment makes the path optimization more efficient and effective. On the other hand, the cost function revision makes the resulting paths meet human driving behavior, such as less backward driving and motion direction changes. In addition, rigorous simulations and analysis demonstrate the effectiveness of the cost function revision and the state space adjustment and illustrate good performance of the proposed approach in common and even complex parking scenarios.

Autonomous parking techniques can be used to tackle the lacking problem of parking spaces. In this paper, a sampling-based motion planner consisting of optimizing bidirectional rapidly-exploring random trees* (Bi-RRT*) and parking-oriented model predictive control (MPC) is proposed to properly deal with various parking scenarios. The optimal Bi-RRT* approach aims to improve the common defects of traditional sampling-based motion planners, such as uncertainties of path quality and consistency, and exploring inefficiency in narrow spaces. For this reason, the proposed motion planner is able to overcome strict environments with obstacles and narrow spaces. The parking-oriented MPC is then designed for steering and speed controls simultaneously for accurately and smoothly tracking parking paths. Furthermore, the proposed controller is dedicated to work under the practical scenarios, such as vehicle considerations, realtime control, and signal delay. To verify the effects of the proposed autonomous parking system, extensive simulations and experiments are conducted in common and strict parking scenarios, such as perpendicular parking, parallel parking. The simulation results not only verify the effects of each technical element, but also show the capability to deal with the various parking scenarios. Furthermore, various on-car experiments sufficiently demonstrate that the proposed system can be actually implemented in everyday life. INDEX TERMS Autonomous parking system, sampling-based motion planning, parking-oriented vehicle control, bidirectional rapidly-exploring random trees* (Bi-RRT*), model predictive control (MPC), perpendicular parking, parallel parking.

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Due to advances in wireless communication technologies, wireless transmissions gradually replace traditional wired data transmissions. In recent years, vehicles on the move can also enjoy the convenience of wireless communication technologies by assisting each other in message exchange and form an interconnecting network, namely Vehicular Ad Hoc Networks (VANETs). In a VANET, each vehicle is capable of communicating with nearby vehicles and accessing information provided by the network. There are two basic communication models in VANETs, V2V and V2I. Vehicles equipped with wireless transceiver can communicate with other vehicles (V2V) or roadside units (RSUs) (V2I). RSUs acting as gateways are entry points to the Internet for vehicles. Naturally, vehicles tend to choose nearby RSUs as serving gateways. However, due to uneven density distribution and high mobility nature of vehicles, load imbalance of RSUs can happen. In this paper, we study the RSU load-balancing problem and propose two solutions. In the first solution, the whole network is divided into sub-regions based on RSUs’ locations. A RSU provides Internet access for vehicles in its sub-region and the boundaries between sub-regions change dynamically to adopt to load migration. In the second solution, vehicles choose their serving RSUs distributedly by taking their future trajectories and RSUs’ loading information into considerations. From simulation results, the proposed methods can improve packet delivery ratio, packet delay, and load balance among RSUs.

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