The prediction of surrounding traffic participants behavior is a crucial and challenging task for driver assistance and autonomous driving systems. Today's approaches mainly focus on modeling dynamic aspects of the traffic situation and try to predict traffic participants behavior based on this. In this article we take a first step towards extending this common practice by calculating location-specific a-priori lane change probabilities. The idea behind this is straight forward: The driving behavior of humans may vary in exactly the same traffic situation depending on the respective location. E.g. drivers may ask themselves: Should I pass the truck in front of me immediately or should I wait until reaching the less curvy part of my route lying only a few kilometers ahead? Although, such information is far away from allowing behavior prediction on its own, it is obvious that today's approaches will greatly benefit when incorporating such location-specific a-priori probabilities into their predictions. For example, our investigations show that highway interchanges tend to enhance driver's motivation to perform lane changes, whereas curves seem to have lane change-dampening effects. Nevertheless, the investigation of all considered local conditions shows that superposition of various effects can lead to unexpected probabilities at some locations. We thus suggest dynamically constructing and maintaining a lane change probability map based on customer fleet data in order to support onboard prediction systems with additional information. For deriving reliable lane change probabilities a broad customer fleet is the key to success.
翻译:对周围交通参与者行为的预测是司机协助和自主驾驶系统的关键和具有挑战性的任务。 今天的做法主要侧重于模拟交通形势的动态方面,并试图根据这一点预测交通参与者的行为。 在本篇文章中,我们迈出第一步,通过计算特定地点的优先航道变化概率来扩大这一共同做法。 背后的想法是直截了当的: 人的驱动行为可能因不同的交通情况而异,这取决于不同的交通地点。 例如,司机可能问自己: 我是否应该立即通过在我前面的卡车,或者我是否应该等到到达我路线上仅停留在几公里外的不太曲折的部分? 虽然这些信息远远不能允许自己进行行为预测,但显而易见的是,如果将这种特定地点的优先概率纳入他们的预测中,那么今天的做法将大有裨益。 例如,我们的调查显示,公路交换往往会提高司机进行车道变化的动机,而曲线似乎具有路道变化的标志效果。 尽管如此,对所有当地条件的调查表明,各种影响都比自己预测更远,因此,在将预测中,将会出现一种不可预测的概率,因此,我们可以预测机队的机队会更加稳定地进行。