For autonomous vehicles and driver assist systems,path planning and collision avoidance algorithms benefit from accurate predictions of future location of other vehicles and intent of their drivers.In the literature,t...For autonomous vehicles and driver assist systems,path planning and collision avoidance algorithms benefit from accurate predictions of future location of other vehicles and intent of their drivers.In the literature,the algorithms that provide driver intent belong to two categories:those that use physics based models with some type of filtering,and machine learning based approaches.In this paper we employ barrier functions(BF)to decide driver intent.BFs are typically used to prove safety by establishing forward invariance of an admissible set.Here,we decide if the“target”vehicle is violating one or more possibly fictitious(i.e.,non-physical)barrier constraints determined based on the context provided by the road geometry.The algorithm has a very small computational footprint and better false positive and negative rates than some of the alternatives.The predicted intent is then used by a control barrier function(CBF)based collision avoidance system to prevent unnecessary interventions,for either an autonomous or human-driven vehicle.展开更多
我国老年驾驶人数量持续增长,驾驶人结构的变化给交通安全带来了挑战。相比于其他年龄段驾驶人,老年人生心理功能逐渐衰退,更容易发生交通事故。认知功能与驾驶安全表现显著相关。从注意反应能力、执行处理能力、空间感知能力3项认知功...我国老年驾驶人数量持续增长,驾驶人结构的变化给交通安全带来了挑战。相比于其他年龄段驾驶人,老年人生心理功能逐渐衰退,更容易发生交通事故。认知功能与驾驶安全表现显著相关。从注意反应能力、执行处理能力、空间感知能力3项认知功能领域出发,研究老年人驾驶特征,设计驾驶模拟实验风险事件,获得认知驾驶行为数据,分析青年人、中年人、老年人驾驶行为特征的差异性;采用主客观结合的方法确定指标权重,提出认知驾驶行为指数计算方法;以驾驶人属性和认知功能为自变量,以认知驾驶行为指数为因变量,建立广义线性混合模型,探究不同因素对认知驾驶能力的影响。结果表明年龄、周驾驶频率、自我调节和TMT-B(Trail Making TestB)与认知驾驶行为指数显著相关,MMSE(Mini-Mental State Examination)为边缘显著相关;老年驾驶人的认知驾驶行为指数受个体特质影响较大;相较于老年人,青年人认知驾驶行为指数更差,中年人更好;周驾驶频率低的人比周驾驶频率高的人认知驾驶行为指数更好;自我调节频率为低和中的驾驶人,比频率为高的驾驶人认知驾驶行为指数更好;TMT-B测量认知正常的驾驶人比认知障碍驾驶人的认知驾驶行为指数更好。该研究从交通事故的人因机理角度出发,探究老年驾驶人面对的认知挑战,提出老年人认知驾驶行为指数计算方法并解析影响因素,为简化老年人驾驶适宜性评价程序、制定驾驶安全干预策略提供参考。展开更多
在硬件在回路仿真系统中,模型需与数据采集卡之间进行实时数据的采集和交换,Simulink下Real Time Workspace工具箱提供了与硬件之间的实时数据采集和交换,但是工具箱并不支持市面上所有采集板卡的类型,给用户对采集板卡选型带来一定局...在硬件在回路仿真系统中,模型需与数据采集卡之间进行实时数据的采集和交换,Simulink下Real Time Workspace工具箱提供了与硬件之间的实时数据采集和交换,但是工具箱并不支持市面上所有采集板卡的类型,给用户对采集板卡选型带来一定局限性。因此本文使用Simulink中S-Function模块编写不同采集板卡的硬件驱动,证明只要是数据采集工具箱支持的板卡型号,都可以用S-Function进行模块化硬件驱动编写,解决了Simulink中Real Time Workspace工具箱给用户在使用Simulink建模过程中对板卡选型的局限性这一问题。展开更多
文摘For autonomous vehicles and driver assist systems,path planning and collision avoidance algorithms benefit from accurate predictions of future location of other vehicles and intent of their drivers.In the literature,the algorithms that provide driver intent belong to two categories:those that use physics based models with some type of filtering,and machine learning based approaches.In this paper we employ barrier functions(BF)to decide driver intent.BFs are typically used to prove safety by establishing forward invariance of an admissible set.Here,we decide if the“target”vehicle is violating one or more possibly fictitious(i.e.,non-physical)barrier constraints determined based on the context provided by the road geometry.The algorithm has a very small computational footprint and better false positive and negative rates than some of the alternatives.The predicted intent is then used by a control barrier function(CBF)based collision avoidance system to prevent unnecessary interventions,for either an autonomous or human-driven vehicle.
文摘我国老年驾驶人数量持续增长,驾驶人结构的变化给交通安全带来了挑战。相比于其他年龄段驾驶人,老年人生心理功能逐渐衰退,更容易发生交通事故。认知功能与驾驶安全表现显著相关。从注意反应能力、执行处理能力、空间感知能力3项认知功能领域出发,研究老年人驾驶特征,设计驾驶模拟实验风险事件,获得认知驾驶行为数据,分析青年人、中年人、老年人驾驶行为特征的差异性;采用主客观结合的方法确定指标权重,提出认知驾驶行为指数计算方法;以驾驶人属性和认知功能为自变量,以认知驾驶行为指数为因变量,建立广义线性混合模型,探究不同因素对认知驾驶能力的影响。结果表明年龄、周驾驶频率、自我调节和TMT-B(Trail Making TestB)与认知驾驶行为指数显著相关,MMSE(Mini-Mental State Examination)为边缘显著相关;老年驾驶人的认知驾驶行为指数受个体特质影响较大;相较于老年人,青年人认知驾驶行为指数更差,中年人更好;周驾驶频率低的人比周驾驶频率高的人认知驾驶行为指数更好;自我调节频率为低和中的驾驶人,比频率为高的驾驶人认知驾驶行为指数更好;TMT-B测量认知正常的驾驶人比认知障碍驾驶人的认知驾驶行为指数更好。该研究从交通事故的人因机理角度出发,探究老年驾驶人面对的认知挑战,提出老年人认知驾驶行为指数计算方法并解析影响因素,为简化老年人驾驶适宜性评价程序、制定驾驶安全干预策略提供参考。
文摘在硬件在回路仿真系统中,模型需与数据采集卡之间进行实时数据的采集和交换,Simulink下Real Time Workspace工具箱提供了与硬件之间的实时数据采集和交换,但是工具箱并不支持市面上所有采集板卡的类型,给用户对采集板卡选型带来一定局限性。因此本文使用Simulink中S-Function模块编写不同采集板卡的硬件驱动,证明只要是数据采集工具箱支持的板卡型号,都可以用S-Function进行模块化硬件驱动编写,解决了Simulink中Real Time Workspace工具箱给用户在使用Simulink建模过程中对板卡选型的局限性这一问题。
