Cut-in maneuver recognition and behavior generation using Bayesian networks and fuzzy logic
Cut-in maneuver recognition and behavior generation using Bayesian networks and fuzzy logic
复制标题
使用贝叶斯网络和模糊逻辑进行切入机动识别和行为生成
DOI：
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发表时间：
2012
期刊：
影响因子：
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通讯作者：
S. Nedevschi
中科院分区：
文献类型：
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作者：
Voichita Popescu;S. Nedevschi
本文提出了一种利用贝叶斯网络识别切入操作的方法，随后对该情形的危险程度进行了评估。利用模糊逻辑控制器生成了本车的适当行为。该控制器的任务是提供适当的动作，比如通过触发紧急制动来帮助防止因危险的切入操作而可能导致的碰撞，同时确保车内乘客的高度舒适性，即避免触发错误的紧急制动。模糊控制器分别为人机界面（HMI）和可能的执行器提供定性和定量的值。定性值以被动警告的形式为本车提供避免即将发生的碰撞的适当反应。定量值是适当的制动值，执行器应使用这些值来避免防撞操作。该解决方案基于立体视觉感知系统提供的环境信息。实验表明，该解决方案能够利用不确定的传感器数据进行推理和决策，总体性能良好。
In this paper a method for recognizing the cutin maneuver using a Bayesian network is proposed, followed by an evaluation of the degree of danger of the situation. An adequate behavior of the ego-vehicle is generated using a fuzzy logic controller. The task of this controller is to provide appropriate actions such as to help prevent a possible collision caused by a dangerous cut-in maneuver, by issuing an emergency brake, as well as to assure a high degree of comfort to the vehicle passengers, i.e. to avoid triggering false emergency brakes. The fuzzy controller provides qualitative and quantitative values, for a HMI and possible actuators, respectively. The qualitative values provide the appropriate response of the ego-vehicle for avoiding an imminent collision, in the form of passive warnings. The quantitative values are the adequate brake values, which should be used by actuators, in order to avoid a collision avoidance maneuver. The solution is based on the environment information provided by a stereovision perception system. The experiments show the solution is able to reason and make decision using the uncertain sensorial data, providing an overall good performance.