Traction adaptive motion planning and control has potential to improve an an automated vehicle's ability to avoid accident in a critical situation. However, such functionality require an accurate friction estimate for the road ahead of the vehicle that is updated in real time. Current state of the art friction estimation techniques include high accuracy local friction estimation in the presence of tire slip, as well as rough classification of the road surface ahead of the vehicle, based on forward looking camera. In this paper we show that neither of these techniques in isolation yield satisfactory behavior when deployed with traction adaptive motion planning and control functionality. However, fusion of the two provides sufficient accuracy, availability and foresight to yield near optimal behavior. To this end, we propose a fusion method based on heteroscedastic gaussian process regression, and present initial simulation based results.

翻译：电车公司的适应性机动规划和控制有可能提高自动车辆在危急情况下避免事故的能力,然而,这种功能要求对车辆前面的道路进行准确的摩擦估计,并实时加以更新。目前的最新摩擦估计技术包括:在轮胎滑块出现时进行高精度的当地摩擦估计,以及根据前视摄影机对车辆前面的道路表面进行粗略分类。在本文件中,我们表明,在采用牵引性机动规划和控制功能时,这些孤立技术都没有产生令人满意的行为。但是,这两种技术的结合提供了足够的准确性、可用性和预见性,足以产生接近最佳的行为。为此,我们提议了一种基于超常功率的粗略轨迹回归的聚合方法,并提出了初步模拟结果。