考虑驾驶员避撞行为特性的汽车前方防碰撞系统研究
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摘要
资料显示，有70%~90%的交通事故是由于驾驶员操作失误所致。而汽车前方防碰撞系统属于一种先进汽车安全驾驶辅助技术，通过使用雷达等前方监视技术的系统传感器，在作出碰撞可能性很高的判断时，会给驾驶员发出警报，催促驾驶员的制动操作，甚至当作出无法避免撞击的判断时，即使没有驾驶员的制动操作，也会主动采取制动，降低车辆撞击时的速度。因此，该系统能有效减少驾驶员误操作，实现避免或削弱车辆追尾碰撞危险，对提高交通安全具有重要意义。基于其对提高汽车主动安全性方面的显著作用，因此前方防碰撞系统应对措施决策、相关控制算法开发以及具备对驾驶员个体差异的自适应性等方面已成为现阶段国内外汽车工程领域研究的热点。
本文基于当前国际上对汽车前方防碰撞系统的研究现状，并结合所涉及的相关理论和技术热点问题，系统地实施了从车辆虚拟跟随防碰撞中驾驶员制动时刻特性分析及制动时刻特性建模、系统功能定义、预警和自动制动功能策略及自动制动控制量决策算法开发与仿真验证，乃至实车试验系统软硬件、高压油供给单元及其控制器电路研制、性能演示样车试制、场地试验验证等一系列汽车前方防碰撞系统研发工作。其研究目标为基于驾驶员在车辆虚拟跟随防碰撞中操控行为特性分析及建模结果，设计汽车前方防碰撞系统的两级预警/自动制动功能策略，利用提出的驾驶员制动时刻个体差异动态特性及时自适应方法和基于灰靶分析法的最优制动减速度决策，开发出具备及时自适应驾驶员个体差异的防追尾预警和仿驾驶员避撞行为的自动制动功能系统原理样机，使该系统能够在一定程度上满足驾驶员个性化或特殊需求，降低虚警率、误警率，促进汽车前方防碰撞系统发展与完善，从而提高行车安全及通行效率。
论文首先利用汽车驾驶模拟器进行了接近静止前车、接近低速前车和跟随减速前车3种高危追尾工况下嵌入真实驾驶员的防碰撞试验，分析驾驶员按照不同防碰撞要求采取相应制动操作的作用时刻信息与人的因素（年龄、性别）和环境因素（本车和前车运动状态信息）在内的多个研究参数的关联性，提出基于驾驶员采取最大制动减速度时刻的车辆安全行驶状态判断指标，并分别建立该指标下的驾驶员采取习惯制动减速度和强制动减速度制动时刻的BP神经网络模型，通过对比验证说明所建模型具有较高预测精度，表明其可用于理解驾驶员防追尾碰撞行为特征，为其认知和追尾危险判断行为的不确定性和非线性特征难以描述问题提供了一个新的解答方案，也为阐明驾驶员在跟随防追尾碰撞中安全判断机理、高效前方防碰撞系统开发提供理论依据。
其次，为使建立的驾驶员习惯制动时刻神经网络模型能够及时自适应实际驾驶中不同驾驶员、其不同阶段的制动行为特征，提出基于误差反传算法的模型参数调整方法；且为保证模型能达到满意的调整效果，采用驾驶员习惯制动时刻正误分析法将经分析后能正确描述驾驶员制动时刻行为特征的数据构成训练样本集，应用于模型参数调整过程，并利用驾驶员在干燥和冰雪切换连续路面上虚拟跟随行驶数据验证上述方法的有效性，结果表明模型经参数调整后预测准确性显著提高。故基于此模型制定的初级预警功能策略能够有效降低由于动态环境因素和驾驶员自身的瞬时因素对系统预警性能的影响，且提高了模型预测的动态适应性。
再次，针对在防碰撞过程中系统提供的自动制动功能与驾驶员采取的紧急转向防碰撞行为之间可能存在的相互干涉问题，利用对驾驶员转向防碰撞行为特性分析结果，建立了依据最小舒适安全转向防碰撞距离的系统自动制动功能执行策略；而对于系统自动制动功能工作中制动控制量如何确定问题，以驾驶员在实际交通情况下判断、决策的思维模式作为防碰撞制动减速度最优决策算法制定依据，提出了基于灰靶分析法的最优制动减速度决策算法；并利用驾驶员采取的实际制动数据，采用粒子群优化对算法中最优目标权重进行离线辨识，通过对比验证表明决策算法得到的最优制动减速度与驾驶员采取的实际减速度较接近；根据最优制动减速度算法的决策结果，再结合基于PID控制理论设计的控制器和建立的车辆逆向制动系统模型，可得到期望制动压力，从而为前方防碰撞系统执行有效的自动制动提供保障。
然后，以常规车辆结构为基础，对高压油供给单元及其控制器、前方目标雷达信息处理、预警功能的预警方式选取等方面进行设计开发，搭建实车试验平台，并保证平台具有良好的可靠性，以用于汽车前方防碰撞系统的相关算法验证及性能测试。
最后，基于以上研究成果，通过仿真及实车试验对汽车前方防碰撞系统总体功能及各项关键技术的研究结果进行验证。结果表明，提出的系统两级预警和自动制动功能策略以及自动制动控制量决策能有效实现对车辆的合理防碰撞报警和防碰撞自动制动，系统体现出良好的驾驶辅助性能，且能够体现驾驶员紧急防碰撞行为特征。
Traffic safety has always been the most serious problem in current society, statisticsshow that70%to90%of accidents are caused by pilot operational errors. Forward collisionavoidance system is an advanced vehicle safety driving assistance technology, which canprovide warning to the driver when making a high likelihood of collision judge, and promotethe driver’s braking operation, even if the driver does not brake, it will still provideautomatic braking to reduce the speed of the vehicle during the impact. Therefore, the risk ofrear-end collision can be effectively avoided, and traffic safety will be improved. Due to itssignificant role in improving automotive active safety, research and development ondecision-making and control algorithm of the system with good adaptability to the drivershave become hot issues in automotive engineering field.
According to the present development status of forward collision avoidance system inthe world and combined with the related theory and technical issues, a set of development offront anti-collision system have been studied in this paper, which include analysis andmodeling of drivers’ braking moment characteristics in virtual car following collisionavoidance scenes, definitions of system function, pre-crash warning and automatic brakingdecision-making and development and simulation verification of automatic braking controlalgorithm, development of control system hardware circuit/software, design of printed circuitboard, trail-manufacture of prototype and field test.
Based on results of analysis and modeling of drivers braking moment characteristic, theultimate goal of this research is to design anti-collision warning and automatic brakingfunction strategy for forward collision avoidance system. This paper will establish the timelyadaptive method of individual dynamic characteristic difference, propose braking controlvariable decision algorithm based on gray target analysis, develop a prototype with thesystem of collision alarm and automatic braking function which is equipped with adaptivedriver’s individual difference and carry out the system in real car to verify. The system isable to meet the specific needs of the driver personalization, to reduce the false alarm rate, topromote the development and improvement of early warning systems and automatic brakingfunction, and further to improve the traffic safety and efficiency.
Three kinds of accident conditions of embedded with real driver’s rear-end crash tests,including approaching the front stationary vehicle, the low-speed vehicle and following thefront slow down vehicle, were conducted by the using of car driving simulator. Corrrelationamong the driver action time information based on the different requirement of theanti-collision braking operation to take, human factors (age, sex) and envirmental factors(motion state information of leading and following vehicles) were studied. Vehicle safetydriving indicator at the time of maximum braking deceleration moment is proposed, BPneural network models were established according to the indicator at the normal and harddriver braking deceleration moment, which will further contribute to understand the behaviorof drivers anti-collision action characteristics, and provide a new feasible solution for theuncertainty and nonlinear characteristics of human cognition and dangerous judge behaviorof rear-end that are difficult to describe. But also it provided a theoretical basis for thesystem development.
In order to make the pre-warning function adapt to the braking moment characteristicsof different drivers, different braking stages, the aim of this stddy is to improve theperformance of driver assistance systems. An approach of normal braking decelerationmoment of BP neural network model’s weight parameters timely adjustment was proposedbased on error back propagation algorithm. In order to ensure that the selected trainingsamples can correctly describe the driver braking moment characteristics, the driver normalbraking moment of identification analysis method was presented, so a number of processeddata was used to constitute the training sample set. Therefore, the developed warningstrategy has better robustness, and thus improves the prediction model’s dynamicadaptability.
To solve the possible problem of mutual interference between the automatic brakingfunction and the driver to take emergency steering in the anti-collision process, automaticbraking control algorithm was developed by analyzing the characteristics of the driver’semergency steering behavior, which considered a minimum comfort safe distance. Optimalbraking control amount decision algorithm based on the gray theory is proposed to realizeautomatic braking function, which was referenced to the thinking mode of drivers’identification and decision. Then the optimal weights of algorithm were identified accordingto driver’s collision avoidance behavior characteristic by using particle swarm optimization,so reasonable premise for braking control decisions were provided. Based on the optimaldecision result and combined with the establishment of vehicle reverse braking systemmodel, the desired brake pressure was obtained to provide a guarantee for implementing effective anti-collision braking.
Furthermore, to validate the effectiveness of main research achievements and systemfunctions, a real vehicle test platform for the system, based on the structure of a conventionalvehicle, was constructed. Several key technologies are developed to improve the flexibilityand reliability of the platform, which included electro-hydraulic braking actuators andcontrollers, radar information processing method, the way of warning selection and so on.
Finally, based on these studies above, simulation and real vehicle tests were carried out.The experimental results show that two-stage warning and automatic braking function isreasonable, and the system has the ability to meet the driver braking behavior characteristics.
本文基于当前国际上对汽车前方防碰撞系统的研究现状，并结合所涉及的相关理论和技术热点问题，系统地实施了从车辆虚拟跟随防碰撞中驾驶员制动时刻特性分析及制动时刻特性建模、系统功能定义、预警和自动制动功能策略及自动制动控制量决策算法开发与仿真验证，乃至实车试验系统软硬件、高压油供给单元及其控制器电路研制、性能演示样车试制、场地试验验证等一系列汽车前方防碰撞系统研发工作。其研究目标为基于驾驶员在车辆虚拟跟随防碰撞中操控行为特性分析及建模结果，设计汽车前方防碰撞系统的两级预警/自动制动功能策略，利用提出的驾驶员制动时刻个体差异动态特性及时自适应方法和基于灰靶分析法的最优制动减速度决策，开发出具备及时自适应驾驶员个体差异的防追尾预警和仿驾驶员避撞行为的自动制动功能系统原理样机，使该系统能够在一定程度上满足驾驶员个性化或特殊需求，降低虚警率、误警率，促进汽车前方防碰撞系统发展与完善，从而提高行车安全及通行效率。
论文首先利用汽车驾驶模拟器进行了接近静止前车、接近低速前车和跟随减速前车3种高危追尾工况下嵌入真实驾驶员的防碰撞试验，分析驾驶员按照不同防碰撞要求采取相应制动操作的作用时刻信息与人的因素（年龄、性别）和环境因素（本车和前车运动状态信息）在内的多个研究参数的关联性，提出基于驾驶员采取最大制动减速度时刻的车辆安全行驶状态判断指标，并分别建立该指标下的驾驶员采取习惯制动减速度和强制动减速度制动时刻的BP神经网络模型，通过对比验证说明所建模型具有较高预测精度，表明其可用于理解驾驶员防追尾碰撞行为特征，为其认知和追尾危险判断行为的不确定性和非线性特征难以描述问题提供了一个新的解答方案，也为阐明驾驶员在跟随防追尾碰撞中安全判断机理、高效前方防碰撞系统开发提供理论依据。
其次，为使建立的驾驶员习惯制动时刻神经网络模型能够及时自适应实际驾驶中不同驾驶员、其不同阶段的制动行为特征，提出基于误差反传算法的模型参数调整方法；且为保证模型能达到满意的调整效果，采用驾驶员习惯制动时刻正误分析法将经分析后能正确描述驾驶员制动时刻行为特征的数据构成训练样本集，应用于模型参数调整过程，并利用驾驶员在干燥和冰雪切换连续路面上虚拟跟随行驶数据验证上述方法的有效性，结果表明模型经参数调整后预测准确性显著提高。故基于此模型制定的初级预警功能策略能够有效降低由于动态环境因素和驾驶员自身的瞬时因素对系统预警性能的影响，且提高了模型预测的动态适应性。
再次，针对在防碰撞过程中系统提供的自动制动功能与驾驶员采取的紧急转向防碰撞行为之间可能存在的相互干涉问题，利用对驾驶员转向防碰撞行为特性分析结果，建立了依据最小舒适安全转向防碰撞距离的系统自动制动功能执行策略；而对于系统自动制动功能工作中制动控制量如何确定问题，以驾驶员在实际交通情况下判断、决策的思维模式作为防碰撞制动减速度最优决策算法制定依据，提出了基于灰靶分析法的最优制动减速度决策算法；并利用驾驶员采取的实际制动数据，采用粒子群优化对算法中最优目标权重进行离线辨识，通过对比验证表明决策算法得到的最优制动减速度与驾驶员采取的实际减速度较接近；根据最优制动减速度算法的决策结果，再结合基于PID控制理论设计的控制器和建立的车辆逆向制动系统模型，可得到期望制动压力，从而为前方防碰撞系统执行有效的自动制动提供保障。
然后，以常规车辆结构为基础，对高压油供给单元及其控制器、前方目标雷达信息处理、预警功能的预警方式选取等方面进行设计开发，搭建实车试验平台，并保证平台具有良好的可靠性，以用于汽车前方防碰撞系统的相关算法验证及性能测试。
最后，基于以上研究成果，通过仿真及实车试验对汽车前方防碰撞系统总体功能及各项关键技术的研究结果进行验证。结果表明，提出的系统两级预警和自动制动功能策略以及自动制动控制量决策能有效实现对车辆的合理防碰撞报警和防碰撞自动制动，系统体现出良好的驾驶辅助性能，且能够体现驾驶员紧急防碰撞行为特征。
Traffic safety has always been the most serious problem in current society, statisticsshow that70%to90%of accidents are caused by pilot operational errors. Forward collisionavoidance system is an advanced vehicle safety driving assistance technology, which canprovide warning to the driver when making a high likelihood of collision judge, and promotethe driver’s braking operation, even if the driver does not brake, it will still provideautomatic braking to reduce the speed of the vehicle during the impact. Therefore, the risk ofrear-end collision can be effectively avoided, and traffic safety will be improved. Due to itssignificant role in improving automotive active safety, research and development ondecision-making and control algorithm of the system with good adaptability to the drivershave become hot issues in automotive engineering field.
According to the present development status of forward collision avoidance system inthe world and combined with the related theory and technical issues, a set of development offront anti-collision system have been studied in this paper, which include analysis andmodeling of drivers’ braking moment characteristics in virtual car following collisionavoidance scenes, definitions of system function, pre-crash warning and automatic brakingdecision-making and development and simulation verification of automatic braking controlalgorithm, development of control system hardware circuit/software, design of printed circuitboard, trail-manufacture of prototype and field test.
Based on results of analysis and modeling of drivers braking moment characteristic, theultimate goal of this research is to design anti-collision warning and automatic brakingfunction strategy for forward collision avoidance system. This paper will establish the timelyadaptive method of individual dynamic characteristic difference, propose braking controlvariable decision algorithm based on gray target analysis, develop a prototype with thesystem of collision alarm and automatic braking function which is equipped with adaptivedriver’s individual difference and carry out the system in real car to verify. The system isable to meet the specific needs of the driver personalization, to reduce the false alarm rate, topromote the development and improvement of early warning systems and automatic brakingfunction, and further to improve the traffic safety and efficiency.
Three kinds of accident conditions of embedded with real driver’s rear-end crash tests,including approaching the front stationary vehicle, the low-speed vehicle and following thefront slow down vehicle, were conducted by the using of car driving simulator. Corrrelationamong the driver action time information based on the different requirement of theanti-collision braking operation to take, human factors (age, sex) and envirmental factors(motion state information of leading and following vehicles) were studied. Vehicle safetydriving indicator at the time of maximum braking deceleration moment is proposed, BPneural network models were established according to the indicator at the normal and harddriver braking deceleration moment, which will further contribute to understand the behaviorof drivers anti-collision action characteristics, and provide a new feasible solution for theuncertainty and nonlinear characteristics of human cognition and dangerous judge behaviorof rear-end that are difficult to describe. But also it provided a theoretical basis for thesystem development.
In order to make the pre-warning function adapt to the braking moment characteristicsof different drivers, different braking stages, the aim of this stddy is to improve theperformance of driver assistance systems. An approach of normal braking decelerationmoment of BP neural network model’s weight parameters timely adjustment was proposedbased on error back propagation algorithm. In order to ensure that the selected trainingsamples can correctly describe the driver braking moment characteristics, the driver normalbraking moment of identification analysis method was presented, so a number of processeddata was used to constitute the training sample set. Therefore, the developed warningstrategy has better robustness, and thus improves the prediction model’s dynamicadaptability.
To solve the possible problem of mutual interference between the automatic brakingfunction and the driver to take emergency steering in the anti-collision process, automaticbraking control algorithm was developed by analyzing the characteristics of the driver’semergency steering behavior, which considered a minimum comfort safe distance. Optimalbraking control amount decision algorithm based on the gray theory is proposed to realizeautomatic braking function, which was referenced to the thinking mode of drivers’identification and decision. Then the optimal weights of algorithm were identified accordingto driver’s collision avoidance behavior characteristic by using particle swarm optimization,so reasonable premise for braking control decisions were provided. Based on the optimaldecision result and combined with the establishment of vehicle reverse braking systemmodel, the desired brake pressure was obtained to provide a guarantee for implementing effective anti-collision braking.
Furthermore, to validate the effectiveness of main research achievements and systemfunctions, a real vehicle test platform for the system, based on the structure of a conventionalvehicle, was constructed. Several key technologies are developed to improve the flexibilityand reliability of the platform, which included electro-hydraulic braking actuators andcontrollers, radar information processing method, the way of warning selection and so on.
Finally, based on these studies above, simulation and real vehicle tests were carried out.The experimental results show that two-stage warning and automatic braking function isreasonable, and the system has the ability to meet the driver braking behavior characteristics.
引文
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