In order to drive safely in a dynamic environment, autonomous vehicles should be able to predict the future states of traffic participants nearby, especially surrounding vehicles, similar to the capability of predictive driving of human drivers. That is why researchers are devoted to the field of trajectory prediction and propose different methods. This paper is to provide a comprehensive and comparative review of trajectory-prediction methods proposed over the last two decades for autonomous driving. It starts with the problem formulation and algorithm classification. Then, the popular methods based on physics, classic machine learning, deep learning, and reinforcement learning are elaborately introduced and analyzed. Finally, this paper evaluates the performance of each kind of method and outlines potential research directions to guide readers.

This paper suggests a constrained H/sub /spl infin// control scheme for active suspensions with output and control constraints. The H/sub /spl infin// performance is used to measure ride comfort so that more general road disturbances can be considered. Time-domain constraints, representing requirements for: 1) good road holding which may have an impact on safety; 2) suspension stroke limitation; and 3) avoidance of actuator saturation, are captured using the concept of reachable sets and state-space ellipsoids. The proposed approach can potentially achieve the best possible ride comfort by allowing constrained variables free as long as they remain within given bounds. A state feedback solution to the constrained H/sub /spl infin// active suspension control problem is derived in the framework of linear matrix inequality (LMI) optimization and multiobjective control. Analysis and simulation results for a two-degree-of-freedom (2-DOF) quarter-car model show possible improvements on ride comfort, while respecting time-domain hard constraints.

As a typical example of cyber-physical systems (CPSs), intelligent vehicles are receiving increasing attention, and the obstacle avoidance problem for such vehicles has become a hot topic of discussion. This paper presents a simultaneous trajectory planning and tracking controller for use under cruise conditions based on a model predictive control (MPC) approach to address obstacle avoidance for an intelligent vehicle. The reference trajectory is parameterized as a cubic function in time and is determined by the lateral position and velocity of the intelligent vehicle and the velocity and yaw angle of the obstacle vehicle at the start point of the lane change maneuver. Then, the control sequence for the vehicle is incorporated into the expression for the reference trajectory that is used in the MPC optimization problem by treating the lateral velocity of the intelligent vehicle at the end point of the lane change as an intermediate variable. In this way, trajectory planning and tracking are both captured in a single MPC optimization problem. To evaluate the effectiveness of the proposed simultaneous trajectory planning and tracking approach, joint veDYNA-Simulink simulations were conducted in the unconstrained and constrained cases under leftward and rightward lane change conditions. The results illustrate that the proposed MPC-based simultaneous trajectory planning and tracking approach achieves acceptable obstacle avoidance performance for an intelligent vehicle.

With the tremendous success of machine learning (ML), concerns about their black-box nature have grown. The issue of interpretability affects trust in ML systems and raises ethical concerns such as algorithmic bias. In recent years, the feature attribution explanation method based on Shapley value has become the mainstream explainable artificial intelligence approach for explaining ML models. This paper provides a comprehensive overview of Shapley value-based attribution methods. We begin by outlining the foundational theory of Shapley value rooted in cooperative game theory and discussing its desirable properties. To enhance comprehension and aid in identifying relevant algorithms, we propose a comprehensive classification framework for existing Shapley value-based feature attribution methods from three dimensions: Shapley value type, feature replacement method, and approximation method. Furthermore, we emphasize the practical application of the Shapley value at different stages of ML model development, encompassing pre-modeling, modeling, and post-modeling phases. Finally, this work summarizes the limitations associated with the Shapley value and discusses potential directions for future research.