基于驾驶行为的车道偏离预警系统关键技术研究
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摘要
车道偏离预警系统是驾驶辅助系统的重要的组成部分,通过报警(震动、声音等形式)辅助驾驶员减少汽车因车道偏离而发生的道路交通事故,具有重大的现实意义。车道偏离现象产生的根源是由于驾驶员控制的车辆不能很好地跟随道路标志标线,而目前的车道偏离预警系统侧重以车道识别判定车道偏离程度,忽略对于驾驶行为和车辆响应特性的研究,因而存在误警率高、适应性差的缺点。
论文从驾驶行为分析入手,深入研究在车道偏离过程中驾驶人、车辆与道路环境之间的内在逻辑关系,建立了基于驾驶行为的车道偏离预警系统。预警系统通过图像传感器、车辆参数传感器等,实现了驾驶人、道路环境以及车辆运行参数的感知,完成基于车道偏离的环境感知技术研究,构建基于车道偏离的方向控制模型,实现预期车道偏离评价。结合车道偏离评价和转向意图预估实现车道偏离综合评价体系的构建,实现安全决策,使驾驶员随时了解车辆运行状态和行车环境状况,采取合理的驾驶操作,保证行车安全。
行车环境感知技术是车道偏离预警系统的关键所在,车道偏离环境感知技术主要包括车道偏离检测、转向意图检测和转向反应时间检测；在结构化道路假设的基础上提取感兴趣区域,实现分区域多模型拟合,实现了车道标线的高精度检测；基于车辆转向结构参数和驾驶人脸转向特征检测共同判定驾驶人转向意图；采取生理学的评价指标定性关联转向反应时间和注意力状态,同时实现实车下基于多通道融合的驾驶人注意力状态检测。
论文在预瞄-跟随理论的基础上,引入基于车道偏离的控制环境,对于驾驶行为的各环节进行简化建模,提出一种简化的用于方向控制的驾驶人模型：以驾驶人的前视角作为驾驶人的信号感知源,假设驾驶视角恒定,建立基于二自由度汽车模型的车辆偏离行驶方向模型,提出转向盘转向增益的数学模型,构建基于车道偏离的方向控制模型；并提出了相应的模型控制策略,在Carsim与Matlab/Simulink联合仿真的汽车虚拟试验环境下验证了模型的可行性。
论文提出模型综合评价指标的控制策略,基于遗传算法自主完成了转向增益与预瞄距离的参数辨识；论文还进一步分析了车速、转向反应时间、预瞄距离等不同参数条件下的模型对于汽车操作稳定性的影响。
论文构建了车道偏离综合评价体系,包括现状车道偏离评价、预期车道偏离评价、转向意图评价,。在此基础上构建了SVM-Adaboost强分类器,用于综合评价。现状车道偏离评价反映了目前的车道偏离状态,预期车道偏离评价反映了未来一段时间内的车道偏离趋势,转向意图评价则用于识别驾驶意图,三者结合构建的车道偏离预警系统能有效降低误警率。
最后,论文在驾驶模拟器上试验验证了综合评价体系的合理性。
LDWS (Lane departure warning system) is one of the most important components of driver assistance system, which assists driver to reduce road traffic accidents caused by lane departure, by means of alarm (vibration, sound and so on). Thus, it is of great practical significance. The root reason of lane departure lies in driver's failure to follow the lane. However, current LDWS mainly focuses on the lane departure level estimate through lane detection, but overlooks the researches on driving behavior and vehicle response characteristics, resulting in a high rate of error warning and poor adaptability.
Beginning with the driving behavior, the logical relationship among driver, vehicle and road environment during lane departure is analyzed thoroughly, and a LDWS based on driving behavior is established. The warning system can acquire information from driver, road environment and vehicle operating parameters by image sensors and vehicle parameter sensors, and the environmental sensing technology based on lane departure is realized. Meanwhile, direction control model is constructed to achieve the predicted evaluation on lane departure. Combing with the evaluation on lane departure and estimate on steering intention, comprehensive evaluation system on lane departure is constructed, which meets the requirement to make the driver keep abreast of the vehicle running status and environmental condition and take appropriate driving measures to ensure safety.
Driving environment sensing technology is the key to LDWS. Environment sensing technology of lane departure mainly includes the detection on lane departure, steering intention and response time of steering. Region of interest is proposed based on the assumption of structured road to achieve sub-regional multi-model fitting, in which precision detection of lane markings is achieved. Steering intention is determined according to the structural parameters of vehicle steering and the detection on steering feature of driver's face. Physiology evaluation is utilitied to determine the correlation between steering response time and attention state, completing the detection on driver's attention in use of multi-channel information fusion.
On the basis of the preview-follow theory, control environment which is based on lane departure is introduced, simplification and modeling of driver behavior are conducted, and a simplified driver model for directional control is proposed. Driver's front view is selected as signal preception source and the driving view is assumed to be constant. The driving direction model based on two degrees of freedom vehicle model is established. The mathematical model of steering gain is proposed, and direction control model based on LDWS is built. Corresponding control strategy is put forward, and the feasibility of this model is verified in the automotive virtual test environment based on CarSim&Matlab/Simulink co-simulation.
Control strategy of comprehensive evaluation criteria is proposed. Parameter identification of steering gain and preview distance is achieved independently by Genetic Algorithms. Furthermore, the influence of different parameters (vehicle speed, steering reaction time and distance preview) on vehicle stability is analyzed.
Comprehensive evaluation system of lane departure is constructed, including lane departure evaluation, predicted lane departure evaluation, steering intention evaluation. A SVM-Adaboost strong classifier is built for overall comprehensive evaluation. Lane departure evaluation reflects the current condition of lane departure, predicted lane departure evaluation reflects the trend of lane departure in the next period of time, and steering intention evaluation is used to identify driver's intention. LDWS containing all three evaluation methods above can effectively reduce the rate of error warning.
Finally, the rationality of evalutation system is verified in driving simulator.
论文从驾驶行为分析入手,深入研究在车道偏离过程中驾驶人、车辆与道路环境之间的内在逻辑关系,建立了基于驾驶行为的车道偏离预警系统。预警系统通过图像传感器、车辆参数传感器等,实现了驾驶人、道路环境以及车辆运行参数的感知,完成基于车道偏离的环境感知技术研究,构建基于车道偏离的方向控制模型,实现预期车道偏离评价。结合车道偏离评价和转向意图预估实现车道偏离综合评价体系的构建,实现安全决策,使驾驶员随时了解车辆运行状态和行车环境状况,采取合理的驾驶操作,保证行车安全。
行车环境感知技术是车道偏离预警系统的关键所在,车道偏离环境感知技术主要包括车道偏离检测、转向意图检测和转向反应时间检测；在结构化道路假设的基础上提取感兴趣区域,实现分区域多模型拟合,实现了车道标线的高精度检测；基于车辆转向结构参数和驾驶人脸转向特征检测共同判定驾驶人转向意图；采取生理学的评价指标定性关联转向反应时间和注意力状态,同时实现实车下基于多通道融合的驾驶人注意力状态检测。
论文在预瞄-跟随理论的基础上,引入基于车道偏离的控制环境,对于驾驶行为的各环节进行简化建模,提出一种简化的用于方向控制的驾驶人模型：以驾驶人的前视角作为驾驶人的信号感知源,假设驾驶视角恒定,建立基于二自由度汽车模型的车辆偏离行驶方向模型,提出转向盘转向增益的数学模型,构建基于车道偏离的方向控制模型；并提出了相应的模型控制策略,在Carsim与Matlab/Simulink联合仿真的汽车虚拟试验环境下验证了模型的可行性。
论文提出模型综合评价指标的控制策略,基于遗传算法自主完成了转向增益与预瞄距离的参数辨识；论文还进一步分析了车速、转向反应时间、预瞄距离等不同参数条件下的模型对于汽车操作稳定性的影响。
论文构建了车道偏离综合评价体系,包括现状车道偏离评价、预期车道偏离评价、转向意图评价,。在此基础上构建了SVM-Adaboost强分类器,用于综合评价。现状车道偏离评价反映了目前的车道偏离状态,预期车道偏离评价反映了未来一段时间内的车道偏离趋势,转向意图评价则用于识别驾驶意图,三者结合构建的车道偏离预警系统能有效降低误警率。
最后,论文在驾驶模拟器上试验验证了综合评价体系的合理性。
LDWS (Lane departure warning system) is one of the most important components of driver assistance system, which assists driver to reduce road traffic accidents caused by lane departure, by means of alarm (vibration, sound and so on). Thus, it is of great practical significance. The root reason of lane departure lies in driver's failure to follow the lane. However, current LDWS mainly focuses on the lane departure level estimate through lane detection, but overlooks the researches on driving behavior and vehicle response characteristics, resulting in a high rate of error warning and poor adaptability.
Beginning with the driving behavior, the logical relationship among driver, vehicle and road environment during lane departure is analyzed thoroughly, and a LDWS based on driving behavior is established. The warning system can acquire information from driver, road environment and vehicle operating parameters by image sensors and vehicle parameter sensors, and the environmental sensing technology based on lane departure is realized. Meanwhile, direction control model is constructed to achieve the predicted evaluation on lane departure. Combing with the evaluation on lane departure and estimate on steering intention, comprehensive evaluation system on lane departure is constructed, which meets the requirement to make the driver keep abreast of the vehicle running status and environmental condition and take appropriate driving measures to ensure safety.
Driving environment sensing technology is the key to LDWS. Environment sensing technology of lane departure mainly includes the detection on lane departure, steering intention and response time of steering. Region of interest is proposed based on the assumption of structured road to achieve sub-regional multi-model fitting, in which precision detection of lane markings is achieved. Steering intention is determined according to the structural parameters of vehicle steering and the detection on steering feature of driver's face. Physiology evaluation is utilitied to determine the correlation between steering response time and attention state, completing the detection on driver's attention in use of multi-channel information fusion.
On the basis of the preview-follow theory, control environment which is based on lane departure is introduced, simplification and modeling of driver behavior are conducted, and a simplified driver model for directional control is proposed. Driver's front view is selected as signal preception source and the driving view is assumed to be constant. The driving direction model based on two degrees of freedom vehicle model is established. The mathematical model of steering gain is proposed, and direction control model based on LDWS is built. Corresponding control strategy is put forward, and the feasibility of this model is verified in the automotive virtual test environment based on CarSim&Matlab/Simulink co-simulation.
Control strategy of comprehensive evaluation criteria is proposed. Parameter identification of steering gain and preview distance is achieved independently by Genetic Algorithms. Furthermore, the influence of different parameters (vehicle speed, steering reaction time and distance preview) on vehicle stability is analyzed.
Comprehensive evaluation system of lane departure is constructed, including lane departure evaluation, predicted lane departure evaluation, steering intention evaluation. A SVM-Adaboost strong classifier is built for overall comprehensive evaluation. Lane departure evaluation reflects the current condition of lane departure, predicted lane departure evaluation reflects the trend of lane departure in the next period of time, and steering intention evaluation is used to identify driver's intention. LDWS containing all three evaluation methods above can effectively reduce the rate of error warning.
Finally, the rationality of evalutation system is verified in driving simulator.
引文
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