The main aim of the project is to dynamic control design for automated driving of vision-based autonomous vehicles, with a particular focus on the coordinated steering and braking control in emergency obstacle avoidance. An autonomous vehicle uses a complex multi-input and multi-output structure, which possesses the features of parameter uncertainties and strong nonlinearities, and the fixed phenomena of lateral and longitudinal dynamics are clear in a combined cornering and braking movement. The effective coordinated control system for automated driving is wished-for to deal with these coupled and nonlinear features and eliminate the disturbances. First, a vision algorithm is constructed to detect the orientation path and offer the local location information between vehicles and reference path in real time. Then, a novel coordinated steering and braking control approach is proposed based on the nonlinear back stepping control theory and the adaptive fuzzy sliding-mode control method. Finally, experimental tests evident that the proposed control strategy possesses encouraging tracking performance and enhances the riding comfort and constancy of autonomous vehicles.

Nonlinear Coordinated Steering and Braking Control of Vision-Based Autonomous Vehicles in Emergency Obstacle Avoidance

An X-by-wire chassis can improve the kinematic characteristics of human-vehicle closed-loop system and thus active safety especially under emergency scenarios via enabling chassis coordinated control. This paper aims to provide a complete and systematic survey on chassis coordinated control methods for full X-by-wire vehicles, with the primary goal of summarizing recent reserch advancements and stimulating innovative thoughts. Driving condition identification including driver's operation intention, critical vehicle states and road adhesion condition and integrated control of X-by-wire chassis subsystems constitute the main framework of a chassis coordinated control scheme. Under steering and braking maneuvers, different driving condition identification methods are described in this paper. These are the trigger conditions and the basis for the implementation of chassis coordinated control. For the vehicles equipped with steering-by-wire, braking-by-wire and/or wire-controlled-suspension systems, state-of-the-art chassis coordinated control methods are reviewed including the coordination of any two or three chassis subsystems. Finally, the development trends are discussed.

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Chassis Coordinated Control for Full X-by-Wire Vehicles-A Review

A study pointed out that the delay time of the driver’s nervous system has a significant effect on the roll stability of the vehicle. However, the existing researches on vehicle rollover prevention control rarely consider the influence of driver factors on vehicle roll stability. Aiming at this problem, a vehicle roll stability and path tracking control strategy considering driver in the loop is proposed to assist different types of drivers. It includes the supervisory decision layer and execution layer. The supervisory decision layer selects the corresponding control mode according to the driver’s steering wheel angle change rate, path tracking deviation and vehicle roll stability information. The execution layer includes three modes: human-machine shared steering, active braking and integrated chassis control. The human-machine shared steering and active braking modes assist the driver to improve the roll stability and path tracking accuracy. The integrated chassis control mode is used for the automatic driving of vehicles under emergency conditions. Simulation results show that the proposed control strategy can effectively improve the vehicle roll stability and path tracking accuracy, and reduce the driver’s operating burden.

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Roll Stability and Path Tracking Control Strategy Considering Driver in the Loop

This paper develops a novel integrated collision avoidance strategy for autonomous vehicles in an emergency based on steering and braking. Specifically, the framework of the collision avoidance strategy is composed of two parts: an up-level decision-making layer and a low-level controller layer. The purpose of the up-level is to select the appropriate control strategy based on the vehicle information, and the low-level is to drive the vehicle according to the instructions generated by the up-level. More concretely, a novel control strategy is proposed by integrating four-wheel steering, active rear steering, and differential braking with guaranteed path-tracking accuracy and driving stability by adaptive model predictive control (AMPC). Finally, extensive co-simulations in MATLAB/Simulink and CarSim are conducted to verify the effectiveness of the proposed collision avoidance strategy in terms of tracking error, yaw rate, and roll angle. 

/pdf/emergency-collision-avoidance-strategy-for-autonomous-1ah0o7av.pdf

Emergency collision avoidance strategy for autonomous vehicles based on steering and differential braking

With the advancement on automated vehicle technology, the highly automated driving is expected to work in complicated traffic situations such as urban intersections and traffic incidents. However, the traffic incidents still remain a comprehensively challenging situation especially in the mixed traffic. To resolve this problem, a sophisticated control strategy is required to support the automated driving in these unexpected traffic situations. From this background, this study mainly focuses on developing a generalized control strategy to manage the automated vehicles driving in traffic incident situations. First, the representative scenarios are analyzed, and a generalized control strategy is elaborated based on the framework of cooperative driving automation. The proposed control strategy can be extended to more complicated situations, and it is expected to extend the operational design domain of automated vehicles.

Control Strategy for Cooperative Driving Automation in Traffic Incidents

Aiming at the issue of active obstacle avoidance control for intelligent vehicles under emergency, an active safety collaborative control system integrated with adaptive cruise control (ACC), rear steering control (RSC), and rollover braking control (RBC) is developed. The proposed novel control architecture consists of a supervisor, an upper controller, and a lower controller. The supervisor decides which control mode to be used based on multi-sensors information of the driving vehicle. Active rear wheel steering based on lateral acceleration feedback and active braking control strategies based on fuzzy PID are added into the control model to enhance driving stability. To make the vehicle motion control model suitable for highway path tracking with obstacle avoidance under emergency, a priority is given to the RBC controller even though the vehicle speed is lower than the expected speed when the rollover index exceeds the safety threshold. A nonlinear vehicle simulation model is established to validate the proposed control scheme based on Carsim. Finally, a Carsim-Simulink co-simulation model is constructed, and simulation results show that the proposed integrated control strategy based on active safety collaborative control has good performance in both path tracking and driving stability, and has better stability of rollover under emergency.

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Research on Active Obstacle Avoidance Control Strategy for Intelligent Vehicle Based on Active Safety Collaborative Control

In this paper, we propose a strategy to solve the problems of obstacle in path planning and tracking for automated vehicles. First, an improved ant colony algorithm considering vehicle parameters and stability is introduced for path planning with obstacle avoidance environment. Second, PID method is improved for vehicle path tracking by introducing motion control containing steering control and speed control. Moreover, some cooperative braking control strategies based on fuzzy PID are added into the motion control to enhance path tracking and handing performance under emergency obstacle avoidance. Simulation results show that the proposed ant colony algorithm can be used to plan the collision-free path and the fuzzy PID motion controller work well in both speed and path following and has a better stability of rollover under emergency.

Jiufei Luo

Papers

Research on Active Obstacle Avoidance Control Strategy for Intelligent Vehicle Based on Active Safety Collaborative Control

Path Planning and Tracking Strategy under Emergency for Vehicle Obstacle Avoidance System