Hazard-evaluation-oriented Moving Horizon Parallel Steering Control for Driver-Automation Collaboration During Automated Driving

Prompted by emerging developments in connected and automated vehicles, parallel steering control, one aspect of parallel driving, has become highly important for intelligent vehicles for easing the burden and ensuring the safety of human drivers. This paper presents a parallel steering control framework for an intelligent vehicle using moving horizon optimization. The framework considers lateral stability, collision avoidance and actuator saturation and describes them as constraints, which can blend the operation of a human driver and a parallel steering controller effectively. Moreover, the road hazard and the steering operation error are employed to evaluate the operational hazardous of an intelligent vehicle. Under the hazard evaluation, the intelligent vehicle will be mainly operated by the human driver when the vehicle operates in a safe and stable manner. The automated steering driving objective will play an active role and regulate the steering operations of the intelligent vehicle based on the hazard evaluation. To verify the effectiveness of the proposed hazard-evaluation-oriented moving horizon parallel steering control approach, various validations are conducted, and the results are compared with a parallel steering scheme that does not consider automated driving situations. The results illustrate that the proposed parallel steering controller achieves acceptable performance under both conventional conditions and hazardous conditions.      

汽车转向/防抱死制动协同控制

为了解决汽车转向过程中防抱死制动稳定性问题,提出一种新的协同控制系统.该协同控制结构由转向控制器和制动控制器组成.在转向控制中设计滑模鲁棒自适应控制器和横摆力矩控制器力求改善汽车动态响应,鲁棒自适应性和稳定性.此外定义协同误差,建立汽车协同误差模型并设计汽车防抱死制动鲁棒自适应控制系统.为了减少转向系统和制动系统之间的补偿控制律难以确定的困难,提出耦合误差补偿原理与同一给定控制相结合的新的耦合控制策略.最后用仿真结果验证所设计控制算法的有效性.     

Integrated vehicle control through the coordination of longitudinal/lateral and vertical dynamics controllers: Flatness and LPV/‐based design

This paper deals with global chassis control of automotive vehicles. It focuses on the coordination of suspension and steering/braking vehicle controllers based on the interaction between the vertical and lateral behaviors of the vehicle. It is shown that the lateral acceleration and resulting roll motion of the car generate load transfers that considerably affect vehicle stability. A control law is designed in hierarchical way to improve the overall dynamics of the vehicle and cope with coupled driving maneuvers like obstacle avoidance using steering control and stop‐and‐go control using braking or driving wheel torque. This global control strategy includes two types of controllers. The first one is the longitudinal/lateral nonlinear flatness controller. Based on an appropriate choice of flat outputs, the flatness proof of a 3 DOF two‐wheel nonlinear vehicle model is established. Then, the combined longitudinal and lateral vehicle control is designed using algebraic estimation techniques to provide an accurate estimation of the derivatives and filtering of the reference flat outputs. The second part of the proposed strategy consists of a linear parameter‐varying/ suspension controller. This controller uses lateral acceleration as a varying parameter to account for load transfers that directly affect the suspension system. The coordination between the vehicle vertical and lateral dynamics is highlighted in this study, and the linear parameter‐varying/ framework ensures a specific collaborative coordination between the suspension and the steering/braking controllers, to achieve the desired performance. Simulations on a complex full vehicle model have been validated using experimental data obtained on‐board a real Renault Mégane Coupé. Copyright © 2017 John Wiley & Sons, Ltd.     

Shared control for lane departure prevention based on the safe envelope of steering wheel angle

The ability to prevent lane departure has become an important feature for commercialized vehicles. This paper proposes a shared steering assistance strategy based on a safe envelope of steering wheel angle (SWA). This solves the human-machine conflict issue in lane departure prevention (LDP) system which uses steering control to help the driver keep the vehicle within the correct lane. The system combines a driver steering control model, current vehicle states and vehicle-road deviation. The desired SWAs are calculated when the driver intends to drive along the left or right side of the lane, and then the two angles are used to generate the safe envelope. Next, a driver intention estimator is designed to predict driver’s intended SWA and the assistance control is activated by judging whether the driver intended SWA is go beyond the safe envelope. Finally, a H∞ controller and a disturbance observer are developed to determine the assistance torque. In this way, the SWA is limited to safe values to mitigate lane departure and the controller intervention is minimized. The effectiveness of the proposed method is evaluated via numerical simulation with different driving scenarios and human-in-the-loop experiment on a driving simulator. The obtained results show that this method not only can avoid lane departures effectively, but also ensures a good human-machine cooperative performance.     

Receding horizon maneuver generation for automated highway driving

This paper focuses on the problem of decision-making and control in an autonomous driving application for highways. By considering the decision-making and control problem as an obstacle avoidance path planning problem, the paper proposes a novel approach to path planning, which exploits the structured environment of one-way roads. As such, the obstacle avoidance path planning problem is formulated as a convex optimization problem within a receding horizon control framework, as the minimization of the deviation from a desired velocity and lane, subject to a set of constraints introduced to avoid collision with surrounding vehicles, stay within the road boundaries, and abide the physical limitations of the vehicle dynamics. The ability of the proposed approach to generate appropriate traffic dependent maneuvers is demonstrated in simulations concerning traffic scenarios on a two-lane, one-way road with one and two surrounding vehicles.     

Highly-scalable traffic management of autonomous industrial transportation systems

In this paper, we present a novel method for highly-scalable coordination of free-ranging automated guided vehicles in industrial logistics and manufacturing scenarios. The primary aim of this method is to enhance the current industrial state-of-the-art multi-vehicle transportation systems, which, despite their long presence on the factory floor and significant advances over the last decades, still rely on a centralized controller and predetermined network of paths. In order to eliminate the major drawbacks of such systems, including poor scalability, low flexibility, and the presence of a single point of failure, in the proposed control approach vehicles autonomously execute their assigned pick-up and delivery operations by running a fully decentralized control algorithm. The algorithm integrates path planning and motion coordination capabilities and relies on a two-layer control architecture with topological workspace representation on the top layer and state-lattice representation on the bottom layer. Each vehicle plans its own shortest feasible path toward the assigned goal location and resolves conflict situations with other vehicles as they arise along the way. The motion coordination strategy relies on the private-zone mechanism ensuring reliable collision avoidance, and local negotiations within the limited communication radius ensuring high scalability as the number of vehicles in the fleet increases. We present experimental validation results obtained on a system comprising six Pioneer 3DX robots in four different scenarios and simulation results with up to fifty vehicles. We also analyze the overall quality of the proposed traffic management method and compare its performance to other state-of-the-art multi-vehicle coordination approaches.     

城市交叉口车辆速度与交通信号协同优化控制

为了降低城市交通中的行车延误与燃油消耗,针对人类驾驶车辆与自动驾驶车辆混合交通环境,提出一种基于交通信息物理系统(TCPS)的车辆速度与交通信号协同优化控制方法.首先,综合考虑路口交通信号、人类驾驶车辆、自动驾驶车辆三者之间的相互影响,设计一种适用于自动驾驶车辆与人类驾驶车辆混合组队特性的过路口速度规划模型;其次,针对车辆速度规划单一应用时的局限性,即无法减少车辆路口通行延误且易出现无解情况,提出一种双目标协同优化模型,能够综合考虑车辆速度规划与路口交通信号控制,同时降低车辆燃油消耗与路口平均延误.由于双目标优化问题求解的复杂性,设计一种遗传算法-粒子群算法混合求解策略.基于SUMO的仿真实验验证了所提出方法的有效性.     

Autonomous driving at the handling limit using residual reinforcement learning

While driving a vehicle safely at its handling limit is essential in autonomous vehicles in Level 5 autonomy, it is a very challenging task for current conventional methods. Therefore, this study proposes a novel controller of trajectory planning and motion control for autonomous driving through manifold corners at the handling limit to improve the speed and shorten the lap time of the vehicle. The proposed controller innovatively combines the advantages of conventional model-based control algorithm, model-free reinforcement learning algorithm, and prior expert knowledge, to improve the training efficiency for autonomous driving in extreme conditions. The reward shaping of this algorithm refers to the procedure and experience of race training of professional drivers in real time. After training on track maps that exhibit different levels of difficulty, the proposed controller implemented a superior strategy compared to the original reference trajectory, and can to other tougher maps based on the basic driving knowledge learned from the simpler map, which verifies its superiority and extensibility. We believe this technology can be further applied to daily life to expand the application scenarios and maneuvering envelopes of autonomous vehicles.     

Motion planning of autonomous vehicles in a non-autonomous vehicle environment without speed lanes

Planning is one of the key problems for autonomous vehicles operating in road scenarios. Present planning algorithms operate with the assumption that traffic is organised in predefined speed lanes, which makes it impossible to allow autonomous vehicles in countries with unorganised traffic. Unorganised traffic is though capable of higher traffic bandwidths when constituting vehicles vary in their speed capabilities and sizes. Diverse vehicles in an unorganised exhibit unique driving behaviours which are analysed in this paper by a simulation study. The aim of the work reported here is to create a planning algorithm for mixed traffic consisting of both autonomous and non-autonomous vehicles without any inter-vehicle communication. The awareness (e.g. vision) of every vehicle is restricted to nearby vehicles only and a straight infinite road is assumed for decision making regarding navigation in the presence of multiple vehicles. Exhibited behaviours include obstacle avoidance, overtaking, giving way for vehicles to overtake from behind, vehicle following, adjusting the lateral lane position and so on. A conflict of plans is a major issue which will almost certainly arise in the absence of inter-vehicle communication. Hence each vehicle needs to continuously track other vehicles and rectify plans whenever a collision seems likely. Further it is observed here that driver aggression plays a vital role in overall traffic dynamics, hence this has also been factored in accordingly. This work is hence a step forward towards achieving autonomous vehicles in unorganised traffic, while similar effort would be required for planning problems such as intersections, mergers, diversions and other modules like localisation.     

Four wheel steering control by fuzzy approach

This study introduces a fuzzy four-wheel steering control design method for automotive vehicles. After the analysis of some stability aspects of the vehicle lateral motion, including front steering angle variations, the representation of vehicle nonlinear model by Takagi-Sugeno (T-S) fuzzy model is presented. Next, based on the fuzzy model, a fuzzy controller is developed to improve the stability of the vehicle. Sufficient conditions for stability and stabilization of the T-S fuzzy model using fuzzy feedback controllers is given. To demonstrate the effectiveness of the proposed fuzzy controller, simulation results are given showing the performance improvements of the vehicle in terms of the stability and the maneuverability in critical situations.     

The Indirect Shared Steering Control Under Double Loop Structure of Driver and Automation

Due to the critical defects of techniques in fully autonomous vehicles, man-machine cooperative driving is still of great significance in today’s transportation system. Unlike the previous shared control structure, this paper introduces a double loop structure which is applied to indirect shared steering control between driver and automation. In contrast to the tandem indirect shared control, the parallel indirect shared control put the authority allocation system of steering angle into the framework to allocate the corresponding weighting coefficients reasonably and output the final desired steering angle according to the current deviation of vehicle and the accuracy of steering angles. Besides, the active disturbance rejection controller (ADRC) is also added in the frame in order to track the desired steering angle fleetly and accurately as well as restrain the internal and external disturbances effectively which including the steering friction torque, wind speed and ground interference etc. Eventually, we validated the advantages of double loop framework through three sets of double lane change and slalom experiments, respectively. Exactly as we expected, the simulation results show that the double loop structure can effectively reduce the lateral displacement error caused by the driver or the controller, significantly improve the tracking precision and keep great performance in trajectory tracking characteristics when driving errors occur in one of driver and controller.      

A combined reactive and reinforcement learning controller for an autonomous tracked vehicle

Unmanned ground vehicles currently exhibit simple autonomous behaviours. This paper presents a control algorithm developed for a tracked vehicle to autonomously climb obstacles by varying its front and back track orientations. A reactive controller computes a desired geometric configuration based on terrain information. A reinforcement learning algorithm enhances vehicle mobility by finding effective exit strategies in deadlock situations. It is capable of incorporating complex information including terrain and vehicle dynamics through learned experiences. Experiments illustrate the effectiveness of the proposed approach for climbing various obstacles.     

Lane-keeping assistance control algorithm using differential braking to prevent unintended lane departures

This paper describes a hierarchical lane keeping assistance control algorithm for a vehicle. The proposed control strategy consists of a supervisor, an upper-level controller and a lower-level controller. The supervisor determines whether lane departure is intended or not, and whether the proposed algorithm is activated or not. To detect driver′s lane change intention, the steering behavior index has been developed incorporating vehicle speed and road curvature. To validate the detection performance on the lane change intention, full-scale simulator tests on a virtual test track (VTT) are conducted under various driving situations. The upper-level controller is designed to compute the desired yaw rate for the lane departure prevention, and for the guidance with ride comfort. The lower-level controller is designed to compute the desired yaw moment in order to track the desired yaw rate, and to distribute it into each tire′s braking force in order to track the desired yaw moment. The control allocation method is adopted to distribute braking forces under the actuator’s control input limitation. The proposed lane keeping assistance control algorithm is evaluated with human driver model-in-the-loop simulation and experiments on a real vehicle.     

Nonlinear cascade strategy for longitudinal control in automated vehicle guidance

This paper deals with automatic control design for automotive driving with a special focus on the longitudinal control. The automotive vehicle is a complex system characterised by highly nonlinear longitudinal and lateral coupled dynamics. Consequently, the control design for automated driving should deal with both of these dynamic couplings. Indeed, the longitudinal control plays an important role in the automated guidance to ensure safety and comfort of automotive passengers. In this work, a nonlinear cascade longitudinal control based on inner and outer-loops design is proposed. The lateral control is handled following a model predictive approach ensuring the automated steering of the vehicle. Finally, the nonlinear longitudinal control is integrated with the lateral control in a whole architecture to perform a coupled longitudinal and lateral control. The effectiveness of the automated driving strategy is highlighted through simulation results.     

深度纯追随的拟人化无人驾驶转向控制模型

目的 在无人驾驶系统技术中,控制车辆转向以跟踪特定路径是实现驾驶的关键技术之一,大量基于传统控制的方法可以准确跟踪路径,然而如何在跟踪过程中实现类人的转向行为仍是当前跟踪技术面临的挑战性问题之一。现有传统转向模型并没有参考人类驾驶行为,难以实现过程模拟。此外,现有大多数基于神经网络的转向控制模型仅仅以视频帧作为输入,鲁棒性和可解释性不足。基于此,本文提出了一个融合神经网络与传统控制器的转向模型：深度纯追随模型（deep pure pursuit,deep PP）。方法 在deep PP中,首先利用卷积神经网络（convolutional neural network,CNN）提取驾驶环境的视觉特征,同时使用传统的纯追随（pure pursuit,PP）控制器融合车辆运动模型以及自身位置计算跟踪给定的全局规划路径所需的转向控制量。然后,通过拼接PP的转向结果向量和视觉特征向量得到融合特征向量,并构建融合特征向量与人类转向行为之间的映射模型,最终实现预测无人驾驶汽车转向角度。结果 实验将在CARLA（Center for Advanced Research on Language Acquisition）仿真数据集和真实场景数据集上进行,并与Udacity挑战赛的CNN模型和传统控制器进行对比。实验结果显示,在仿真数据集的14个复杂天气条件下,deep PP比CNN模型和传统转向控制器更贴近无人驾驶仪的转向指令。在使用均方根误差（root mean square error,RMSE）作为衡量指标时,deep PP相比于CNN模型提升了50.28%,相比于传统控制器提升了35.39%。最后,真实场景实验验证了提出的模型在真实场景上的实用性。结论 本文提出的拟人化转向模型,综合了摄像头视觉信息、位置信息和车辆运动模型信息,使得无人驾驶汽车的转向行为更贴近人类驾驶行为,并在各种复杂驾驶条件下保持了高鲁棒性。     

基于车流信息的智能巡航控制器的设计分析

设计一个模拟人类驾驶行为的智能巡航控制器。在分析现有巡航控制的基础上,提出利用车流前后车辆的相对速度和间距信息的智能巡航控制算法来选择正确的控制行为,从而平顺跟车行为,使车辆能保持由驾驶员指定的理想跟车距离。仿真结果显示,在此智能巡航控制下能保证车辆和车队在前、后两个方向上的稳定性。     

Lateral control of a backward driven front-steering vehicle

This paper addresses control of the lateral position and orientation of a backward driven front steering vehicle. The low speed associated with backward driving eliminates some challenges but introduces others in lateral control design. While slip angles are small and can be neglected, other small angle approximations can no longer be made. The curvature of the desired path can be very sharp requiring large steering angles. Two control strategies based on a nonlinear kinematic model of the vehicle are developed. One controller is based on input-state feedback linearization with no associated internal dynamics. The other controller incorporates preview and is based on input–output linearization with stable internal dynamics. A major portion of the paper concentrates on experimental implementation of the two controllers using an instrumented Navistar truck. The experimental performance on straight and circular paths as well as on transient curved paths is studied. Good experimental performance is obtained with a spacing accuracy better than 40 cm being achieved in the worst case transient paths. The use of preview turns out to be important with the preview-incorporating controller providing superior performance.     

基于Dubins路径的无人艇运动规划算法

针对无人艇运动规划问题,通过Dubins路径的理论分析,提出一种利用纯粹几何方法的Dubins路径计算方法。该方法中没有出现解方程组的运算,而是首先根据无人艇运动状态计算转向圆,然后利用几何方法计算转向圆间的公切线,最后通过公切线连接得到Dubins路径。通过5组仿真实验验证了所提方法的有效性。前4组仿真实验分别设计了计算Dubins路径过程中可能出现的各种情形,以验证算法适用于多种情况的Dubins路径计算。最后一组仿真实验用于无人艇的路径规划及运动状态调整,仿真结果表明,基于Dubins路径的无人艇运动规划算法是可行的。     

无信号交叉口网联车调度与分布式控制策略

针对多车道无信号交叉口,在高交通流量时易发生拥堵的问题,提出了一种适用于交叉口智能网联车（connected automated vehicle,CAV）通行的解决方案,将问题解耦成顺序决策和分布式控制两个问题。而车辆调度是方案中的关键点,对通行效率有很大的影响,且问题的复杂度是指数级。为解决该问题,提出一种基于队列评价模型的决策方法（queue evaluation spanning tree,QEST）,将网联车存到的多个队列模型中,并以通行效率和车辆延迟建立代价函数,以此对队列进行评价,通过不断循环选择最佳的队列,优化车辆在交叉口的通行顺序,有效提升了通行效率。对第二个问题,提出一种分布式控制框架,使得车辆按预定顺序通过交叉口。结果表明,该方案在中高交通流量时能有效提升交叉口的通行效率并降低车辆延迟和能量消耗。     

The ergonomic value of a bidirectional haptic interface when driving a highly automated vehicle

Advances in technology have fueled the development of driver assistance systems. Even today, these systems can take over parts of the driving task. However, the interface becomes more and more complex with an increasing number of functions. One way to reduce such complexity is to venture the haptic channel. While haptic feedback in lateral direction is comparatively easy to realize via the steering wheel, the longitudinal direction forms a challenge. With conventional control elements, that is, pedals, haptic interaction can only be partially realized (this is due to the division of accelerator and brake pedals). Haptic signals, like forces added to the accelerator pedal, can only transmit information regarding the amount of acceleration, not the desired deceleration. In this context, two-dimensional control elements show great potential regarding future highly automated vehicle driving. Therefore, an experiment conducted at the Institute of Ergonomics of the Technische Universität München investigated the influence of haptic feedback of assistance systems on driving performance when using an active side stick as control element. Additionally, the impact of vehicle vibrations and accelerations were explored. Besides objective performance data, subjective assessment was also reported. The results show that adding assistance significantly improves driving performance. Moreover, subjective ratings indicate a reduction in workload. Accelerations and vibrations, however, had no verifiable effect on the driving performance. This fact was confirmed by the subjects’ subjective assessment. This paper shows that two-dimensional control elements can be a reasonable alternative to steering wheel and pedals when driving a highly automated vehicle.