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.      

Design and evaluation of a model predictive vehicle control algorithm for automated driving using a vehicle traffic simulator

This paper describes the design and evaluation of a model predictive control algorithm for automated driving on a motorway using a vehicle traffic simulator. For the development of a highly automated driving control algorithm, motion planning is necessary to satisfy driving condition in various road traffic situations. There are two key issues in motion planning of automated driving vehicles. One of the key issues is how to handle potentially dangerous situations that could occur in order to guarantee the safety of vehicles. The second key issue is how to guarantee the disturbance rejection of the controller under model uncertainties and external disturbances. To improve safety with respect to the future behaviors of subject vehicles, not the current states but rather the predicted behaviors of surrounding vehicles should be considered. The desired driving mode and a safe driving envelope are determined based on the probabilistic prediction of surrounding vehicles behaviors over a finite prediction horizon. To obtain the desired steering angle and longitudinal acceleration for maintaining the subject vehicle in the safe driving envelope during a finite prediction horizon, a motion planning controller is designed based on an model predictive control (MPC) approach. The developed control algorithm has been successfully implemented on a vehicle electronic control unit (ECU). The proposed control algorithm has been evaluated on a real-time vehicle traffic simulator. The throttle, brake, and steering control inputs and the controlled vehicle behavior have been compared to those of manual driving.     

考虑时延的智能网联汽车混合交通流稳定性分析

为研究时延对交通流稳定性的影响,构建考虑时延的人工驾驶汽车和智能网联汽车混合交通流稳定性分析模型.首先,分析并确定混合交通流中不同类型跟驰模式的比例关系和时延取值;然后,在此基础上采用不同的跟驰参数和时延值区分车辆的跟驰模式,并由此推导出混合交通流线性稳定条件;最后,以智能驾驶员模型为例,通过设计数值实验分别讨论智能网...     

结合状态机和动态目标路径的无人驾驶决策仿真

目的 决策系统是无人驾驶技术的核心研究之一。已有决策系统存在逻辑不合理、计算效率低、应用场景局限等问题,因此提出一种动态环境下无人驾驶路径决策仿真。方法 首先,基于规则模型构建适于无人驾驶决策系统的交通有限状态机;其次,针对交通动态特征,提出基于统计模型的动态目标路径算法计算状态迁移风险;最后,将交通状态机和动态目标路径算法有机结合,设计出一种基于有限状态机的无人驾驶动态目标路径模型,适用于交叉口冲突避免和三车道换道行为。将全速度差连续跟驰模型运用到换道规则中,并基于冲突时间提出动态临界跟车距离。结果 为验证模型的有效性和高效性,对交通环境进行虚拟现实建模,模拟交叉口通行和三车道换道行为,分析文中模型对车流量和换道率的影响。实验结果显示,在交叉口通行时,自主车辆不仅可以检测冲突还可以根据风险评估结果执行安全合理的决策。三车道换道时,自主车辆既可以实现紧急让道,也可以通过执行换道达成自身驾驶期望。通过将实测数据和其他两种方法对比,当车流密度在0.20.5时,本文模型的平均速度最高分别提高32 km/h和22 km/h。当车流密度不超过0.65时,本文模型的换道成功率最高分别提升37%和25%。结论 实验结果说明本文方法不仅可以在动态城区环境下提高决策安全性和正确性,还可以提高车流量饱和度,缓解交通堵塞。     

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。     

交叉路口交通灯实时模糊控制系统设计与实现

提出了一种基于模糊控制的交叉路口交通灯控制系统.该模糊控制系统以单交叉路车长、车长之差为输入,以绿灯延时为输出.并简单介绍了基于单片机的智能交通控制系统的实现.验证计算结果表明,所提出的模糊控制算法能有效地减少交叉口平均车辆延误,为智能交通系统实现提供了一条新途径.     

A digital-driving system for smart vehicles

In the wake of the computer and information technology revolutions, vehicles are undergoing dramatic changes in their capabilities and how they interact with drivers. Although some vehicles can decide to either generate warnings for the human driver or control the vehicle autonomously, they must usually make these decisions in real time with only incomplete information. So, human drivers must still maintain control over the vehicle. I sketch a digital driving behavior model. By simulating and analyzing driver behavior during different maneuvers such as lane changing, lane following, and traffic avoidance, researchers participating in the Beijing Institute of Technology's digital-driving project will be able to examine the possible correlations or causal relations between the smart vehicle, IVISs, the intelligent road-traffic-information network, and the driver. We aim to successfully demonstrate that a digital-driving system can provide a direction for developing human-centered smart vehicles.     

Detailed traffic animation for urban road networks

We present a new agent-based system for detailed traffic animation on urban arterial networks with diverse junctions like signalized crossing, merging and weaving areas. To control the motion of traffic for visualization and animation purposes, we utilize the popular follow-the-leader method to simulate various vehicle types and intelligent driving styles. We also introduce a continuous lane-changing model to imitate the vehicle’s decision-making process and dynamic interactions with neighboring vehicles. By applying our approach in several typical urban traffic scenarios, we demonstrate that our system can well visualize vehicles’ behaviors in a realistic manner on complex road networks and generate immersive traffic flow animations with smooth accelerating strategies and flexible lane changes.     

动态物理拓扑下基于CPS的混合交通牵制控制

随着自动驾驶技术的发展,传统人驾车与自动驾驶车将在一段时间内共存,形成一类新型混合交通.网联自动车的出现为车辆协同行驶提供了新的控制手段,传统的混合交通效率优化控制方法更多地立足于宏观流量的控制或小群体的局部优化, CAV对交通优化作用的潜能还有待开发.鉴于此,首先给出混合交通的信息物理描述,揭示混合交通物理拓扑的动态性;然后提出一种动态物理拓扑下基于CPS的混合交通牵制控制方法,该方法通过调节网联自动车的行驶状态,间接影响、诱导和控制交通中存在的传统人驾车,从而优化整个交通系统.基于Matlab的仿真实验验证了所提出方法的有效性,而基于SUMO的仿真实验表明所提出的牵制控制方法在更真实的场景下依然适用.     

Model predictive control for hybrid vehicle ecological driving using traffic signal and road slope information

This paper presents development of a control system for ecological driving of a hybrid vehicle. Prediction using traffic signal and road slope information is considered to improve the fuel economy. It is assumed that the automobile receives traffic signal information from intelligent transportation systems(ITS). Model predictive control is used to calculate optimal vehicle control inputs using traffic signal and road slope information. The performance of the proposed method was analyzed through computer simulation results. Both the fuel economy and the driving profile are optimized using the proposed approach. It was observed that fuel economy was improved compared with driving of a typical human driving model.     

智能车技术探讨

近年来,环境污染、交通安全及拥堵等严峻的问题困扰着整个世界。智能汽车的出现是为了建立一种新的交通范例,以便在很大程度上降低驾驶人员的劳动强度,避免汽车碰撞和减轻交通拥堵等。因而,发展新一代的智能汽车就成为了世界主要国家的发展战略目标。提出智能汽车的新定义:以自身装备动力驱动的智能信息系统管控的车辆。它具有5大基本功能特征,即车车交互、车人交互、车路交互、车网交互和绿色节能。车、人、路、网4个交互规定了智能汽车和周边环境之间的协作关系,而绿色节能则关注于清洁节约的能源管理系统。同时,讨论分析了支撑智能汽车发展所需要的新理论和新技术。最后,介绍了在缩微环境下进行的智能车研究和实践,并对未来智能汽车产业的发展方向做出展望。     

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.     

Asynchronous control of rotation and translation for a robot vehicle

Robot arms require an ‘arm controller’ to command joint motors to achieve a coordinated motion in an external Cartesian coordinate space. In the same sense, robot vehicles require a ‘vehicle controller’ to command the motors to achieve a coordinated motion specified in terms of an external Cartesian coordinate space. This paper presents the design of a general purpose vehicle controller.

The vehicle controller is designed as a three-layer structure. The top layer is an interpreter which assures a control protocol based on asynhcronous commands and independent control of orientation and forward displacement. The middle layer is a control loop which maintins an estimate of the vehicle's position and orientation, as well as their uncertainties. The control loop generates estimates and commands translation and rotation in terms of a ‘virtual vehicle’. The bottom layer is a translator between the ‘virtual vehicle’ and whatever physical vehicle on which the controller is implemented.     

Protocol design for an automated highway system

A structured use of control, communication and computing techologies in vehicles and in the highway can lead to major increases in highway capacity. Our context is an automated highway system (AHS) in which traffic is organized in platoons of closely spaced vehicles under automatic control. The AHS control tasks are arranged in a three-layer hierarchy. At the top or link layer a centralized controller assigns to each vehicle a path through the highway and sets the target size and speed for platoons to reduce congestion. The remaining two layers are distributed among controllers on each vehicle. A vehicle's platoon layer plans its trajectory to conform to its assigned path and to track the target size. The plan consists of a sequence of elementary maneuvers: merge (combines two platoons into one), split (separates one platoon into two), and change lane (enables a single car to change lane). Once the protocol layer determines that a particular maneuver can safely be initiated, it instructs its regulation layer to execute the corresponding precomputed feedback control law which implements the maneuver. This paper focuses on the design of the platoon layer. In order of ensure that it is safe to initiate a maneuver, the platoon layer controller enters into a negotiation with its neighbors. This negotiation is implemented as a protocol—a structured sequence of message exchanges. After a protocol terminates successfully, the movement of the vehicles involved is coordinated and the maneuver can be initiated. A protocol is designed in two stages. In the first stage, the protocol is described as an informal state machine, one machine per vehicle. The informal state machine does not distinguish between actions and conditions referring to the vehicle's environment and those referring to the protocol itself. In the second stage this distinction is enforced and the protocol machines are specified in the formal language COSPAN. COSPAN software is then used to show that the protocol indeed works correctly. One can now be reasonably confident that, properly implemented, the protocol designed here will work as intended.Work supported by the PATH program, Institute of Transportation Studies, University of California, Berkeley, and NSF Grant ECS-911907. It is a pleasure to thank Dr. Bob Kurshan for providing COSPAN and explaining its use, Ellen Sentovich for help with COSPAN, Max Holm for computer support, Anuj Puri for comments which improved parts of the design, and members of the PATH Seminar on AVCS where this design was first presented and discussed. The authors alone are responsible for the views expressed here.     

Modeling and simulation of overtaking behavior involving environment

Overtaking is a complex driving behavior for intelligent vehicles. Current research on modeling overtaking behavior pays little attention on the effect of environment. This paper focuses on the modeling and simulation of the overtaking behavior in virtual reality traffic simulation system involving environment information, such as road geometry and wind. First, an intelligent vehicle model is proposed to better understand environment information and traffic situation. Then, overtaking behavior model is introduced in detail, the lane changing feasibility is analyzed and the fuzzy vehicle controllers considering the road and wind effect are researched. Virtual reality traffic simulation system is designed to realize the simulation of overtaking behavior, with realistic road geometry features. Finally, simulation results show the correctness and the effectiveness of our approach.     

车辆直接横摆力矩的预测控制研究

针对车辆高速过弯时发生的侧滑问题,将预测控制运用于汽车ESP控制系统中,以2自由度车辆模型为预测内部模型,以车辆直接横摆力矩为输出作用于车轮来控制整车的行驶状态。结合Matlab/Simulink建立的七自由度整车模型以及轮胎模型对所设计的ESP控制器进行分析调整。实验结果表明,预测控制器能很好地控制汽车的横摆角速度和限制质心侧偏角,提高了汽车的稳定性和安全性。     

面向智能驾驶测试的仿真场景构建技术综述

随着汽车智能化程度的不断提高,智能汽车通过环境传感器与周边行驶环境的信息交互与互联更为密切,需应对的行驶环境状况也越来越复杂,包括行驶道路、周边交通和气象条件等诸多因素,具有较强的不确定性、难以重复、不可预测和不可穷尽。限于研发周期和成本、工况复杂多样性,特别是安全因素的考虑,传统的开放道路测试试验或基于封闭试验场的测试难以满足智能驾驶系统可靠性与鲁棒性的测试要求。因此,借助数字虚拟技术的仿真测试成为智能驾驶测试验证一种新的手段,仿真场景的构建作为模拟仿真的重要组成部分,是实现智能驾驶测试中大样本、极限边界小概率样本测试验证的关键技术,这对提升智能驾驶系统的压力和加速测评水平显得尤为重要。面向智能驾驶测试的仿真场景构建技术已成为当前汽车智能化新的研究课题和世界性的研究热点,作为一种新兴技术仍面临许多挑战。本文提出了面向智能驾驶测试的仿真场景构建方法,系统阐述了国内外研究工作的进展与现状,包括场景自动构建方法和交通仿真建模方法,重点分析一些值得深入研究的问题并围绕场景构建技术的发展趋势进行了讨论分析,最后介绍了团队相关研究在2020中国智能驾驶挑战赛仿真赛和世界智能驾驶挑战赛的仿真场景应用情况。     

Design and performance evaluation of mixed manual and automatedcontrol traffic

We present a technology which allows manually controlled and automatically controlled vehicles to co-exist, thereby reducing much of the cost and infrastructure requirements in highway automation. An autonomous vehicle driving system (AVDS) is presented which only uses information from vehicle sensors, thereby eliminating the requirement that vehicles communicate with one another. This format allows gradual market penetration of AVDS, which makes the technology attractive from a systems implementation standpoint. To evaluate the performance of the mixed traffic, we simulate manual and automated vehicles together and show that our AVDS can lead to potentially large gains in capacity and smoother flow without the loss of safety     

越野智能车转向及驱动协调控制

本文以实现智能车在非结构化越野环境中的自主导航为目的,对越野路径识别及轨迹跟踪控制方法开展研究.首先基于视觉传感器和激光传感器实现可行驶路径的分割与提取,并建立了包含车辆转向及驱动的耦合控制系统模型;然后,针对控制系统模型具有非匹配不确定性的特点,采用反演变结构控制算法设计了转向及驱动协调控制器,并引入饱和函数以解决变结构的抖振问题;最后,通过仿真和越野导航试验验证了所提出控制算法的有效性和鲁棒性.     

协同自适应巡航控制车队仿真

如何评估道路环境等外部条件变化对协同自适应巡航控制（Cooperative Adaptive Cruise Control,CACC）车队行驶安全性的影响,对于保障道路交通安全尤为重要。为此,结合Matlab/Simulink和CarSim搭建车辆-环境仿真平台研究道路环境对车队行驶安全性的影响。利用美国NGSIM车辆轨迹数据对平台采用的校正的预瞄驾驶员模型、加速度控制模型、节气门控制模型和制动器控制模型的控制器进行验证;利用平台开展红灯状态、隧道行驶和匝道行驶三种道路环境对车队行驶状态影响的仿真实验。仿真结果表明：车队可以顺利通过路口红绿灯;在安全车速范围内车队进出隧道口时方向控制良好;匝道坡度主要影响车辆加速度和车间距,坡度分别为3%和5%时,车队均保持稳定车间距安全行驶。