This paper presents a preview steering control algorithm and its closed-loop system analysis and experimental validation for accurate, smooth, and computationally inexpensive path tracking of automated vehicles. The path tracking issue is formulated as an optimal control problem with dynamic disturbance, i.e., the future road curvature. A discrete-time preview controller is then designed on the top of a linear augmented error system, in which the disturbances within a finite preview window are augmented as part of the state vector. The obtained optimal steering control law is in an analytic form and consists of two parts: 1) a feedback control responding to tracking errors and 2) a feedforward control dealing with the future road curvatures. The designed control's nature, capacity, computation load, and underlying mechanism are revealed by the analysis of system responses in the time domain and the frequency domain, theoretical steady-state error, and comparison with the model predictive control (MPC). The algorithm was implemented on an automated vehicle platform, a hybrid Lincoln MKZ. The experimental and simulation results are then presented to demonstrate the improved performance in tracking accuracy, steering smoothness, and computational efficiency compared to the MPC and the full-state feedback control.

Eco-driving at signalized intersections has significant potential for energy saving. In this paper, we focus on eco-departure operations of connected vehicles equipped with an internal combustion engine and a step-gear automatic transmission. A Bolza-type optimal control problem (OCP) is formulated to minimize engine fuel consumption. Due to the discrete gear ratio, this OCP is a nonlinear mixed-integer problem, which is challenging to handle by most existing optimization methods. The Legendre pseudospectral method combining the knotting technique is employed to convert it into a multistage interconnected nonlinear programming problem, which then solves the optimal engine torque and transmission gear position. The fuel-saving benefit of the optimized eco-departing operation is validated by a passenger car with a five-speed transmission. For real-time implementation, a near-optimal departing strategy is proposed to quickly determine the behavior of the engine and transmission. When a string of vehicles are departing from an intersection, the acceleration of the leading vehicle(s) should be considered to control the following vehicles. This issue is also addressed in this paper.

Autonomous vehicle (AV) is expected to reshape the future transportation system, and its decision-making is one of the most critical modules. Many current decision-making modules are designed scenario-by-scenario and thus are not capable of meeting high-level AV requirements for lack of scalability to cope with the diversity of drivers’ demands and the infinity of traffic elements. This paper surveys the decision-making design inspired by driver intelligence and environment reasoning with better scalability for future high-level AVs. It involves three aspects: human factors in driving, environment reasoning, and detailed decision methods. The current state of the art of these three sections is surveyed in this paper. The characteristics of distinguished drivers, their decision mechanism, and the factors influencing decision-making are reviewed first to learn from excellent drivers. Environment reasoning is introduced following as a three-layer structure consisting of restriction, interaction, and attention. The optimization-based decision-making algorithms are then reviewed from the aspect of optimization targets, frameworks, applied scenarios, and limitations. Inspired by the existing research on driver intelligence and environment reasoning, a promising decision-making framework is also introduced for high-level AV design.

This paper studies the principles and mechanism of a fuel-optimal strategy in cruising scenarios, i.e., the pulse and glide (PnG) operation, for road vehicles equipped with a step-gear transmission. In the PnG strategy, the control of the engine and the transmission determines the fuel-saving performance, and it is obtained by solving an optimal control problem (OCP). Due to a discrete gear ratio, strong nonlinear engine fuel characteristics, and different dynamics in the pulse/glide mode, the OCP is a switching nonlinear mixed-integer problem. This challenging problem is converted by a knotting technique and the Legendre pseudospectral method to a nonlinear programming problem, which then solves the optimal engine torque and transmission gear position. The optimization results show the significant fuel saving of the PnG operation as compared with the constant-speed cruising strategy. The underlying fuel-saving mechanism of the PnG strategy is explained graphically. For a real-time implementation, a near-optimal practical rule that enables a driver and/or an automatic control system to fast select gear positions and engine torque profile is proposed with only slightly deteriorated fuel saving.

For road transportation, adaptive cruise control (ACC) is a widely used driver assistance technique with partially longitudinal automation. The existing ACC systems usually ignore the road elevation information, which often ends up with large tracking error and poor fuel economy on the roads with up and down slopes. This paper presents a model-based predictive controller (MPC) for fuel-saving ACC to improve the performances on tracking accuracy and fuel consumption by simultaneously considering the road elevation information, nonlinear powertrain dynamics, and spatiotemporal constraint from preceding vehicle. The feasibility of MPC is enhanced by softening nonsafety related hard constraints, and its asymptotical stability is proved by considering the monotonicity property of a properly chosen Lyapunov function. To efficiently compute the optimal control sequence, a pseudospectral discretization technique, instead of evenly spaced discretization, is adopted to transform the continuous-time optimal control problem into a nonlinear programming. Simulation results demonstrate that the proposed method could reduce fuel consumption by 2.65% in a typical driving scenario and significantly improve the tracking accuracy compared to the one without road slope consideration.

Reinforcement learning (RL) can be used to design smart driving policies in complex situations where traditional methods cannot. However, they are frequently black-box in nature, and the resulting policy may perform poorly, including in scenarios where few training cases are available. In this paper, we propose a method to use RL under two conditions: (i) RL works together with a baseline rule-based driving policy; and (ii) the RL intervenes only when the rule-based method seems to have difficulty handling and when the confidence of the RL policy is high. Our motivation is to use a not-well trained RL policy to reliably improve AV performance. The confidence of the policy is evaluated by Lindeberg-Levy Theorem using the recorded data distribution in the training process. The overall framework is named “confidence-aware reinforcement learning” (CARL). The condition to switch between the RL policy and the baseline policy is analyzed and presented. Driving in a two-lane roundabout scenario is used as the application case study. Simulation results show the proposed method outperforms the pure RL policy and the baseline rule-based policy.