Autonomous vehicle field of study has seen considerable researches within three decades. In the last decade particularly, interests in this field has undergone tremendous improvement. One of the main aspects in autonomous vehicle is the path tracking control, focusing on the vehicle control in lateral and longitudinal direction in order to follow a specified path or trajectory. In this paper, path tracking control is reviewed in terms of the basic vehicle model usually used; the control strategies usually employed in path tracking control, and the performance criteria used to evaluate the controller's performance. Vehicle model is categorised into several types depending on its linearity and the type of behaviour it simulates, while path tracking control is categorised depending on its approach. This paper provides critical review of each of these aspects in terms of its usage and disadvantages/advantages. Each aspect is summarised for better overall understanding. Based on the critical reviews, main challenges in the field of path tracking control is identified and future research direction is proposed. Several promising advancement is proposed with the main prospect is focused on adaptive geometric controller developed on a nonlinear vehicle model and tested with hardware-in-the-loop (HIL). It is hoped that this review can be treated as preliminary insight into the choice of controllers in path tracking control development for an autonomous ground vehicle.

Modelling and Control Strategies in Path Tracking Control for Autonomous Ground Vehicles: A Review of State of the Art and Challenges

This paper focuses on the real-time trajectory planning problem for autonomous vehicles driving in realistic urban environments. To solve the complex navigation problem, we adopt a hierarchical motion planning framework. First, a rough reference path is extracted from the digital map using commands from the high-level behavioral planner. The conjugate gradient nonlinear optimization algorithm and the cubic B-spline curve are employed to smoothen and interpolate the reference path sequentially. To follow the refined reference path as well as handle both static and moving objects, the trajectory planning task is decoupled into lateral and longitudinal planning problems within the curvilinear coordinate framework. A rich set of kinematically feasible path candidates are generated to deal with the dynamic traffic both deliberatively and reactively. In the meanwhile, the velocity profile generation is performed to improve driving safety and comfort. After that, the generated trajectories are carefully evaluated by an objective function, which combines behavioral decisions by reasoning about the traffic situations. The optimal collision-free, smooth, and dynamically feasible trajectory is selected and transformed into commands executed by the low-level lateral and longitudinal controllers. Field experiments have been carried out with our test autonomous vehicle on the realistic inner-city roads. The experimental results demonstrated capabilities and effectiveness of the proposed trajectory planning framework and algorithms to safely handle a variety of typical driving scenarios, such as static and moving objects avoidance, lane keeping, and vehicle following, while respecting the traffic rules.

Real-Time Trajectory Planning for Autonomous Urban Driving: Framework, Algorithms, and Verifications

Development of a new integrated local trajectory planning and tracking control framework for autonomous ground vehicles

A method for guidance and control of an autonomous vehicle in problems of border patrolling and obstacle avoidance

Autonomous vehicle (AV) technology can provide a safe and convenient transportation solution for the public, but the complex and various environments in the real world make it difficult to operate safely and reliably. A connected autonomous vehicle (CAV) is an AV with vehicle connectivity capability, which enhances the situational awareness of the AV and enables the cooperation between AVs. Hence, CAV technology can enhance the capabilities and robustness of AV to be a promising transportation solution in the future. This paper introduces a representative architecture of CAVs and surveys the latest research advances, methods, and algorithms for sensing, perception, planning, and control of CAVs. It reviews the state-of-the-art and state-of-the-practice (when applicable) of a multi-layer Perception-Planning-Control architecture including on-board sensors and vehicular communications, the methods of sensor fusion and localization and mapping in the perception layer, the algorithms of decision making and trajectory planning in the planning layer, and the control strategies of trajectory tracking in the control layer. Furthermore, the implementations and impact of vehicle connectivity and the corresponding consequential challenges of cooperative perception, complex connected decision making, and multi-vehicle controls are summarized and their significant research issues enumerated. Most importantly, the critical review in this paper provides a list and discussion of the remaining challenges and unsolved problems of CAVs in each Section which would be helpful to researchers in the field. The comprehensive coverage of this paper makes it particularly useful to academic researchers, practitioners, and students alike.

Research Advances and Challenges of Autonomous and Connected Ground Vehicles

This paper describes a new Autonomous Ground Vehicles (AGVs) trajectory tracking control system towards safe and high-speed operations enabled by incorporating Vehicle Dynamics Control (VDC). The system consists of an AGV desired yaw rate generator based on a kinematic model, and a yaw rate controller based on the vehicle/tyre dynamic models. Sliding Mode Control (SMC) is used to handle the system uncertainties. The performance of the control system was evaluated by using a high-fidelity (experimentally validated) full-vehicle Sport Utility Vehicle (SUV) model provided by CarSim®. Compared with the results of position-error-based AGV control, significant performance improvement was observed.

Autonomous ground vehicle control system for high-speed and safe operation

This paper describes a new trajectory tracking control system for autonomous ground vehicles (AGV) toward safe and high-speed operation enabled by incorporating vehicle dynamics control (VDC) into the AGV. The control system consists of two levels: an AGV desired yaw rate generator based on a kinematic model, and a yaw rate controller based on the vehicle/tire dynamic models. The separation between AGV trajectory tracking and low-level actuation allows the incorporation of the VDC into the AGV systems. Sliding mode control is utilized to handle the system uncertainties. The performance of the proposed control system is evaluated by using a high-fidelity (experimentally validated) full-vehicle sport utility vehicle (SUV) model (rear-drive and front-steer) provided by CarSimregon a race track. Compared with the results for typically-employed position error based AGV control, significant performance improvement is observed.

Joe Steiber

Papers

Autonomous ground vehicle control system for high-speed and safe operation

Autonomous ground vehicle control system for high-speed and safe operation