Emergency steering control of autonomous vehicle for collision avoidance and stabilisation

ABSTRACT

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 large actuator inputs, together with highly nonlinear tyre cornering response. In order to avoid collision while stabilising autonomous vehicle under dynamic driving situations at handling limits, this paper proposes a novel emergency steering control strategy based on hierarchical control architecture consisting of decision-making layer and motion control layer. In decision-making layer, a dynamic threat assessment model continuously evaluates the risk associated with collision and destabilisation, and a path planner based on kinematics and dynamics of vehicle system determines a collision-free path when it suddenly encounters emergency scenarios. In motion control layer, a lateral motion controller considering nonlinearity of tyre cornering response and unknown external disturbance is designed using tyre lateral force estimation-based backstepping sliding-mode control to track a collision-free path, and to ensure the robustness and stability of the closed-loop system. Both simulation and experiment results show that the proposed control scheme can effectively perform an emergency collision avoidance manoeuvre while maintaining the stability of autonomous vehicle in different running conditions.     

Risk management algorithm for rear-side collision avoidance using a combined steering torque overlay and differential braking

This paper describes a risk management algorithm for rear-side collision avoidance. The proposed risk management algorithm consists of a supervisor and a coordinator. The supervisor is designed to monitor collision risks between the subject vehicle and approaching vehicle in the adjacent lane. An appropriate criterion of intervention, which satisfies high acceptance to drivers through the consideration of a realistic traffic, has been determined based on the analysis of the kinematics of the vehicles in longitudinal and lateral directions. In order to assist the driver actively and increase driver's safety, a coordinator is designed to combine lateral control using a steering torque overlay by motor-driven power steering and differential braking by vehicle stability control. In order to prevent the collision while limiting actuator's control inputs and vehicle dynamics to safe values for the assurance of the driver's comfort, the Lyapunov theory and linear matrix inequalities based optimisation methods have been used. The proposed risk management algorithm has been evaluated via simulation using CarSim and MATLAB/Simulink.     

A nonlinear model predictive control formulation for obstacle avoidance in high-speed autonomous ground vehicles in unstructured environments

This paper presents a nonlinear model predictive control (MPC) formulation for obstacle avoidance in high-speed, large-size autono-mous ground vehicles (AGVs) with high centre of gravity (CoG) that operate in unstructured environments, such as military vehicles. The term ‘unstructured’ in this context denotes that there are no lanes or traffic rules to follow. Existing MPC formulations for passenger vehicles in structured environments do not readily apply to this context. Thus, a new nonlinear MPC formulation is developed to navigate an AGV from its initial position to a target position at high-speed safely. First, a new cost function formulation is used that aims to find the shortest path to the target position, since no reference trajectory exists in unstructured environments. Second, a region partitioning approach is used in conjunction with a multi-phase optimal control formulation to accommodate the complicated forms the obstacle-free region can assume due to the presence of multiple obstacles in the prediction horizon in an unstructured environment. Third, the no-wheel-lift-off condition, which is the major dynamical safety concern for high-speed, high-CoG AGVs, is ensured by limiting the steering angle within a range obtained offline using a 14 degrees-of-freedom vehicle dynamics model. Thus, a safe, high-speed navigation is enabled in an unstructured environment. Simulations of an AGV approaching multiple obstacles are provided to demonstrate the effectiveness of the algorithm.     

Comparing and validating models of driver steering behaviour in collision avoidance and vehicle stabilisation

A number of driver models were fitted to a large data set of human truck driving, from a simulated near-crash, low-friction scenario, yielding two main insights: steering to avoid a collision was best described as an open-loop manoeuvre of predetermined duration, but with situation-adapted amplitude, and subsequent vehicle stabilisation could to a large extent be accounted for by a simple yaw rate nulling control law. These two phenomena, which could be hypothesised to generalise to passenger car driving, were found to determine the ability of four driver models adopted from the literature to fit the human data. Based on the obtained results, it is argued that the concept of internal vehicle models may be less valuable when modelling driver behaviour in non-routine situations such as near-crashes, where behaviour may be better described as direct responses to salient perceptual cues. Some methodological issues in comparing and validating driver models are also discussed.     

Improving the robustness of an MPC-based obstacle avoidance algorithm to parametric uncertainty using worst-case scenarios

Previous work by the authors focused on obstacle avoidance in large, high-speed autonomous ground vehicles within unknown and unstructured environments. This work resulted in a nonlinear model predictive control based algorithm that simultaneously optimises both the speed and steering commands. The algorithm can exploit the dynamic limits of the vehicle to navigate it to a target position as quickly as possible without compromising safety. In the algorithm, a model of the vehicle is used explicitly to predict and optimise future actions, but in practice the model parameter values are not known exactly. Thus, in this paper, the robustness of the algorithm to parametric uncertainty is evaluated. It is first demonstrated that using nominal parameter values in the algorithm leads to safety issues in 24% of the evaluated scenarios with the considered parametric uncertainty distributions. To improve the algorithm's robustness, a novel double-worst-case formulation is developed that simultaneously accounts for the robust satisfaction of the two safety requirements of high-speed obstacle avoidance: collision-free and no-wheel-lift-off. Results from simulations with stratified random scenarios and worst-case scenarios show that the double-worst-case formulation renders the algorithm robust to all uncertainty realisations tested. The trade-off between robustness and the task completion performance is also quantified.     

A study on model fidelity for model predictive control-based obstacle avoidance in high-speed autonomous ground vehicles

This paper investigates the level of model fidelity needed in order for a model predictive control (MPC)-based obstacle avoidance algorithm to be able to safely and quickly avoid obstacles even when the vehicle is close to its dynamic limits. The context of this work is large autonomous ground vehicles that manoeuvre at high speed within unknown, unstructured, flat environments and have significant vehicle dynamics-related constraints. Five different representations of vehicle dynamics models are considered: four variations of the two degrees-of-freedom (DoF) representation as lower fidelity models and a fourteen DoF representation with combined-slip Magic Formula tyre model as a higher fidelity model. It is concluded that the two DoF representation that accounts for tyre nonlinearities and longitudinal load transfer is necessary for the MPC-based obstacle avoidance algorithm in order to operate the vehicle at its limits within an environment that includes large obstacles. For less challenging environments, however, the two DoF representation with linear tyre model and constant axle loads is sufficient.     

Design and hardware-in-loop implementation of collision avoidance algorithms for heavy commercial road vehicles

An important aspect from the perspective of operational safety of heavy road vehicles is the detection and avoidance of collisions, particularly at high speeds. The development of a collision avoidance system is the overall focus of the research presented in this paper. The collision avoidance algorithm was developed using a sliding mode controller (SMC) and compared to one developed using linear full state feedback in terms of performance and controller effort. Important dynamic characteristics such as load transfer during braking, tyre-road interaction, dynamic brake force distribution and pneumatic brake system response were considered. The effect of aerodynamic drag on the controller performance was also studied. The developed control algorithms have been implemented on a Hardware-in-Loop experimental set-up equipped with the vehicle dynamic simulation software, IPG/TruckMaker®. The evaluation has been performed for realistic traffic scenarios with different loading and road conditions. The Hardware-in-Loop experimental results showed that the SMC and full state feedback controller were able to prevent the collision. However, when the discrepancies in the form of parametric variations were included, the SMC provided better results in terms of reduced stopping distance and lower controller effort compared to the full state feedback controller.     

Unified decision-making and control for highway collision avoidance using active front steer and individual wheel torque control

ABSTRACT

Collision avoidance is a crucial function for all ground vehicles, and using integrated chassis systems to support the driver presents a growing opportunity in active safety. With actuators such as in-wheel electric motors, active front steer and individual wheel brake control, there is an opportunity to develop integrated chassis systems that fully support the driver in safety critical situations. Here we consider the scenario of an impending frontal collision with a stationary or slower moving vehicle in the same driving lane. Traditionally, researchers have approached the required collision avoidance manoeuver as a hierarchical scheme, which separates the decision-making, path planning and path tracking. In this context, a key decision is whether to perform straight-line braking, or steer to change lanes, or indeed perform combined braking and steering. This paper approaches the collision avoidance directly from the perspective of constrained dynamic optimisation, using a single optimisation procedure to cover these aspects within a single online optimisation scheme of model predictive control (MPC). While the new approach is demonstrated in the context of a fully autonomous safety system, it is expected that the same approach can incorporate driver inputs as additional constraints, yielding a flexible and coherent driver assistance system.     

Optimal motion control for collision avoidance at Left Turn Across Path/Opposite Direction intersection scenarios using electric propulsion

Collision avoidance at intersections involving a host vehicle turning left across the path of an oncoming vehicle (Left Turn Across Path/Opposite Direction) have been studied in the past, but mostly using simplified interventions and rarely considering the possibility of crossing the intersection ahead of a bullet vehicle. Such a scenario where the driver preference is to avoid a collision by crossing the intersection ahead of a bullet vehicle is considered in this work. The optimal vehicle motion for collision avoidance in this scenario is determined analytically using a particle model within an optimal control framework. The optimal manoeuvres are then verified through numerical optimisations using a two-track vehicle model, where it was seen that the wheel forces followed the analytical global force angle result independently of the other wheels. A Modified Hamiltonian Algorithm controller for collision avoidance that uses the analytical optimal control solution is then implemented and tested in CarMaker simulations using a validated Volvo XC90 vehicle model. Simulation results showed that collision risk can be significantly reduced in this scenario using the proposed controller, and that more benefit can be expected in scenarios that require larger speed changes.     

Vehicle rollover avoidance by application of gain-scheduled LQR controllers using state observers

Many researches have been conducted in the area of control applied to vehicle dynamics, aiming at reducing the possibility of the occurrence of the type of accident known as rollover. In this research, based on a common nonlinear model and its linearisation, a method for properly selecting matrices for solving the Riccati equation considering different speeds was proposed. The method showed in which ways speed really influences the choice of controller gains. By developing the dynamic equations for the yaw- and roll-coupled motions and modelling of controllers and state observers, it is possible to compare the efficacy of this control strategy using both linear and nonlinear simulations using Matlab. Significant results were obtained regarding the reduction of the rollover coefficient for a double-lane change manoeuvre at different speeds, thus indicating advantages of using this controller in practical cases.     

Automated driving and autonomous functions on road vehicles

In recent years, road vehicle automation has become an important and popular topic for research and development in both academic and industrial spheres. New developments have received extensive coverage in the popular press, and it may be said that the topic has captured the public imagination. Indeed, the topic has generated interest across a wide range of academic, industry and governmental communities, well beyond vehicle engineering; these include computer science, transportation, urban planning, legal, social science and psychology. While this follows a similar surge of interest – and subsequent hiatus – of Automated Highway Systems in the 1990s, the current level of interest is substantially greater, and current expectations are high. It is common to frame the new technologies under the banner of ‘self-driving cars’ – robotic systems potentially taking over the entire role of the human driver, a capability that does not fully exist at present. However, this single vision leads one to ignore the existing range of automated systems that are both feasible and useful. Recent developments are underpinned by substantial and long-term trends in ‘computerisation’ of the automobile, with developments in sensors, actuators and control technologies to spur the new developments in both industry and academia. In this paper, we review the evolution of the intelligent vehicle and the supporting technologies with a focus on the progress and key challenges for vehicle system dynamics. A number of relevant themes around driving automation are explored in this article, with special focus on those most relevant to the underlying vehicle system dynamics. One conclusion is that increased precision is needed in sensing and controlling vehicle motions, a trend that can mimic that of the aerospace industry, and similarly benefit from increased use of redundant by-wire actuators.     

Formulation and interpretation of optimal braking and steering patterns towards autonomous safety-critical manoeuvres

ABSTRACT

Stability control of a vehicle in autonomous safety-critical at-the-limit manoeuvres is analysed from the perspective of lane keeping or lane changing, rather than that of yaw control as in traditional ESC systems. An optimal control formulation is developed, where the optimisation criterion is a linear combination of the initial and final velocity of the manoeuvre. Varying the interpolation parameter in this formulation turns out to result in an interesting family of optimal braking and steering patterns in stabilising manoeuvres. The two different strategies of optimal lane-keeping control and optimal yaw control are shown to be embedded in the formulation and result from the boundary values of the parameter. The results provide new insights and have the potential to be used for future safety systems that adapt the level of braking to the situation at hand, which is demonstrated through examples of how to exploit theresults.     

汽车防撞系统中目标跟踪与防撞决策研究

为了实现汽车主动安全系统中的目标跟踪与防撞,提出了混合式汽车防撞系统信息融合结构模型,采用分级信息融合实现目标跟踪,推导出了基于跟踪残留误差和预测残留误差共同校正的融合算法,并给出了算法的实现结构;提出了基于局部分析的映射变换方法,实现驾驶模型特征向量连续、实时的修正,在此基础之上,利用模糊积分方法融合多种相关信息,确定汽车应采用的安全运行模式,实现主动安全防撞决策。经过大量试验证明:该算法具有很好的稳定性和准确率。     

Coordinated control strategies for active steering,differential braking and active suspension for vehicle stability,handling and safety improvement

ABSTRACT

In this paper, a coordinated control strategy is proposed to provide an effective improvement in handling stability of the vehicle, safety, and comfortable ride for passengers. This control strategy is based on the coordination among active steering, differential braking, and active suspension systems. Two families of controllers are used for this purpose, which are the high order sliding mode and the backstepping controllers. The control strategy was tested on a full nonlinear vehicle model in the environment of MATLAB/Simulink. Rollover avoidance and yaw stability control constraints have been considered. The control system mainly focuses on yaw stability control. When rollover risk is detected, the proposed strategy controls the roll dynamics to decrease rollover propensity. Simulation results for two different critical driving scenarios, the first one is a double lane change and the other one is a J-turn manoeuvre, show the effectiveness of the coordination strategy in stabilising the vehicle, enhancing handling and reducing rollover propensity.     

Comparing rear wheel steering and rear active differential approaches to vehicle yaw control

A comparison between two different approaches to vehicle stability control is carried out, employing a robust non-parametric technique in the controller design. In particular, an enhanced internal model control strategy, together with a feedforward action and a suitably generated reference map, is employed for the control of a vehicle equipped either with a rear wheel steering (RWS) system or with a rear active differential (RAD) device. The uncertainty arising from the wide range of operating conditions is described by an additive model set employed in the controller design. Extensive steady state and transient tests simulated with an accurate 14 degrees of freedom nonlinear model of the considered vehicle show that both systems are able to improve handling and safety in normal driving conditions. RAD devices can make the vehicle reach higher lateral acceleration values but they achieve only slight stability improvements against oversteer. On the other hand, 4WS systems can greatly improve both vehicle safety and manoeuvrability in all driving situations, making this device an interesting and powerful stability system.     

Evaluation of automotive forward collision warning and collision avoidance algorithms

Collision warning/collision avoidance (CW/CA) systems target a major crash type and their development is a major thrust of the Intelligent Vehicle Initiative. They are a natural extension of adaptive cruise control systems already available on many car models. Many CW/CA algorithms have recently been proposed but the existing literature mainly focuses on algorithm development. Evaluations of these algorithms have been usually based on subjective ratings. The main contribution of this paper is the utilization of a naturalistic driving data set for the evaluation of CW/CA algorithms. We first collect manual driving data from the ICCFOT project, then process the data by Kalman smoothing, and finally identify ‘threatening’ and ‘safe’ data sets according to vehicle brake inputs and vehicle range behavior. Five CW/CA algorithms published in the literature are evaluated against the identified data sets. The performance of these algorithms is determined through a performance metric commonly used in signal detection and information retrieval under unbalanced data population.     

Evaluation of automotive forward collision warning and collision avoidance algorithms

Collision warning/collision avoidance (CW/CA) systems target a major crash type and their development is a major thrust of the Intelligent Vehicle Initiative. They are a natural extension of adaptive cruise control systems already available on many car models. Many CW/CA algorithms have recently been proposed but the existing literature mainly focuses on algorithm development. Evaluations of these algorithms have been usually based on subjective ratings. The main contribution of this paper is the utilization of a naturalistic driving data set for the evaluation of CW/CA algorithms. We first collect manual driving data from the ICCFOT project, then process the data by Kalman smoothing, and finally identify 'threatening' and 'safe' data sets according to vehicle brake inputs and vehicle range behavior. Five CW/CA algorithms published in the literature are evaluated against the identified data sets. The performance of these algorithms is determined through a performance metric commonly used in signal detection and information retrieval under unbalanced data population.     

Pivot point-based control for active rear-wheel steering in passenger vehicles

This paper presents a new application of active rear-wheel steering control to improve the lateral vehicle behaviour. In the state of the art, yaw or lateral velocity is used as control variable that means one degree of freedom being not directly controlled. A worse subjective impressions due to movements in the rear end of the vehicle during strong counter-steering are a consequence. To avoid this effect in urban surroundings, an innovative structure to control the pivot point distance of the vehicle is proposed. In this case the coupled elementary states yaw and lateral velocity can be influenced based on a higher level criteria. Analysis show that pivot point fixing provides a comprehensible reference behaviour. Solving the issue of singularity during disappearing yaw movement is the basis to design a performant modified feedforward input–output linearisation. An analytic stability analysis of the internal dynamics shows system immanent limitations which do not influence the target of improving the lateral vehicle dynamics in urban manoeuvres. Finally, the advantages of pivot-based control are highlighted by a comparison with state of the art rear axle control.     

智能车辆主动避撞与稳定性联合控制仿真

为改善车辆在主动避撞过程中的车辆动力学特性,在MATLAB/Simulink环境下结合驾驶员模型分别设计了基于模糊控制理论的主动避撞控制处理器、制动稳定性控制器和转向稳定性控制器,从而能够在车辆主动避撞过程中综合考虑避撞和稳定性两个目标.并在ADAMS中建立了整车模型,联合MATLAB进行了仿真研究,仿真结果证明,在相...     

汽车防追尾碰撞数学模型研究

为了提高车辆在高速行驶状态下的主动安全性能,研究了处于追尾行驶状态的本车与前车的运动学特征;针对前车的不同运动状态分别推导出了跟车距离的计算模型并分析了模型中3个关键参数的随机性和动态性,对制动迟滞时间提出了基于模糊推理的确定方法,对本车制动减速度和前车的运动加速度提出了比较实用的动态测算公式;另外,研究了防追尾碰撞的控制与执行,建立了动态调整安全制动停车距离的神经网络模型,提出了基于危险裕度判别的安全控制方法。