We survey research on self-driving cars published in the literature focusing on autonomous cars developed since the DARPA challenges, which are equipped with an autonomy system that can be categorized as SAE level 3 or higher. The architecture of the autonomy system of self-driving cars is typically organized into the perception system and the decision-making system. The perception system is generally divided into many subsystems responsible for tasks such as self-driving-car localization, static obstacles mapping, moving obstacles detection and tracking, road mapping, traffic signalization detection and recognition, among others. The decision-making system is commonly partitioned as well into many subsystems responsible for tasks such as route planning, path planning, behavior selection, motion planning, and control. In this survey, we present the typical architecture of the autonomy system of self-driving cars. We also review research on relevant methods for perception and decision making. Furthermore, we present a detailed description of the architecture of the autonomy system of the self-driving car developed at the Universidade Federal do Espirito Santo (UFES), named Intelligent Autonomous Robotics Automobile (IARA). Finally, we list prominent self-driving car research platforms developed by academia and technology companies, and reported in the media.

Self-driving cars: A survey

This paper studies the codesign optimization approach to determine how to optimally adapt automatic control of an intelligent electric vehicle to driving styles. A cyber-physical system (CPS)-based framework is proposed for codesign optimization of the plant and controller parameters for an automated electric vehicle, in view of vehicle's dynamic performance, drivability, and energy along with different driving styles. System description, requirements, constraints, optimization objectives, and methodology are investigated. Driving style recognition algorithm is developed using unsupervised machine learning and validated via vehicle experiments. Adaptive control algorithms are designed for three driving styles with different protocol selections. Performance exploration method is presented. Parameter optimizations are implemented based on the defined objective functions. Test results show that an automated vehicle with optimized plant and controller can perform its tasks well under aggressive, moderate, and conservative driving styles, further improving the overall performance. The results validate the feasibility and effectiveness of the proposed CPS-based codesign optimization approach.

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Driving-Style-Based Codesign Optimization of an Automated Electric Vehicle: A Cyber-Physical System Approach

Adaptive-neural-network-based robust lateral motion control for autonomous vehicle at driving limits

This paper presents a robust fuzzy $H_{\infty }$ control strategy for improving vehicle lateral stability and handling performance through integration of direct yaw moment control system (DYC) and active front steering. Since vehicle lateral dynamics possesses inherent nonlinearities, the main objective is dedicated to deal with the nonlinear challenge in vehicle lateral dynamics by applying Takagi-Sugeno (T-S) fuzzy modeling approach. First, the nonlinear Brush tire dynamics and the nonlinear functions of longitudinal velocity are represented via a T-S fuzzy modeling technique, and vehicle parametric uncertainties are handled by the norm-bounded uncertainties. An uncertain nonlinear vehicle lateral dynamic T-S fuzzy model is then obtained with multi-fuzzy-rules. The resulting robust fuzzy $H_{\infty }$ state-feedback controller is designed with the parallel-distributed compensation strategy and premise variables, and solved via a set of linear matrix inequalities derived from Lyapunov asymptotic stability and quadratic $H_{\infty }$ performance. Simulations for two different maneuvers are implemented with a high-fidelity, CarSimⓇ, full-vehicle model to verify the effectiveness of the developed approach. It is confirmed from the results that the proposed controller can effectively preserve vehicle lateral stability and enhance yaw handling performance.

Improving Vehicle Handling Stability Based on Combined AFS and DYC System via Robust Takagi-Sugeno Fuzzy Control

Neural fuzzy approximation enhanced autonomous tracking control of the wheel-legged robot under uncertain physical interaction

Estimation of tire-road friction coefficient based on combined APF-IEKF and iteration algorithm

The challenging issue of “human–machine copilot” opens up a new frontier to enhancing driving safety. However, driver–machine conflicts and uncertain driver/external disturbances are significant problems in cooperative steering systems, which degrade the system's path-tracking ability and reduce driving safety. This paper proposes a novel stochastic game-based shared control framework to model the steering torque interaction between the driver and the intelligent electric power steering (IEPS) system. A six-order driver–vehicle dynamic system, including driver/external uncertainty, is established for path-tracking. Then, the affine linear-quadratic-based path-tracking problem is proposed to model the maneuvers of the driver and IEPS. Particularly, the feedback Nash and Stackelberg frameworks to the affine-quadratic problem are derived by stochastic dynamic programming. Two cases of copilot lane change driving scenarios are studied via computer simulation. The intrinsic relation between the stochastic Nash and Stackelberg strategies is investigated based on the results. And the steering-in-the-loop experiment reveals the potential of the proposed shared control framework in handling driver–IEPS conflicts and uncertain driver/external turbulence. Finally, the copiloting experiments with a human driver further demonstrate the rationality of the game-based pattern between both the agents.

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Shared Steering Torque Control for Lane Change Assistance: A Stochastic Game-Theoretic Approach

Collision avoidance and stabilisation are two of the most crucial concerns when an autonomous vehicle finds itself in emergency situations, which usually occur in a short time horizon and require l...

Emergency steering control of autonomous vehicle for collision avoidance and stabilisation

Stability as well as robustness is the major concerns in the design of a trajectory tracking controller for an autonomous vehicle. In this paper, a novel lateral stability controller design for vehicle path tracking is developed. First, using dynamic game theory as a general framework, vehicle lateral stability can be viewed as a dynamic difference game so that its two players, namely, the active front steering (AFS) system and active rear steering (ARS) system can work together to provide more stability for vehicle path tracking control. The interactive steering control strategies between AFS and ARS are obtained by noncooperative closed-loop feedback Stackelberg game theory to ensure optimal performance for vehicle path tracking. Then, based on the proposed path-following shared control paradigm, by applying the method of zero-sum game theory, a finite-time robust regulator is developed to make the interaction model more robust to uncertain lateral disturbances. Finally, double-lane change and serpentine driving condition with and without uncertain time-varying lateral disturbance are used to evaluate the proposed control algorithm. Simulation and hardware-in-loop implementation results show that the proposed shared control paradigm based robust path-tracking controller can robustly provide better lateral stability when time-varying lateral disturbances are bounded.

Interactive Control Paradigm-Based Robust Lateral Stability Controller Design for Autonomous Automobile Path Tracking With Uncertain Disturbance: A Dynamic Game Approach

Xuewu Ji

Papers

Adaptive-neural-network-based robust lateral motion control for autonomous vehicle at driving limits

Estimation of tire-road friction coefficient based on combined APF-IEKF and iteration algorithm

Shared Steering Torque Control for Lane Change Assistance: A Stochastic Game-Theoretic Approach

Emergency steering control of autonomous vehicle for collision avoidance and stabilisation

Interactive Control Paradigm-Based Robust Lateral Stability Controller Design for Autonomous Automobile Path Tracking With Uncertain Disturbance: A Dynamic Game Approach