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Bayesian Road Estimation Using Onboard Sensors

机译:使用车载传感器进行贝叶斯道路估计

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This paper describes an algorithm for estimating the road ahead of a host vehicle based on the measurements from several onboard sensors: a camera, a radar, wheel speed sensors, and an inertial measurement unit. We propose a novel road model that is able to describe the road ahead with higher accuracy than the usual polynomial model. We also develop a Bayesian fusion system that uses the following information from the surroundings: lane marking measurements obtained by the camera and leading vehicle and stationary object measurements obtained by a radar–camera fusion system. The performance of our fusion algorithm is evaluated in several drive tests. As expected, the more information we use, the better the performance is.
机译:本文介绍了一种基于多个车载传感器(摄像头,雷达,车轮速度传感器和惯性测量单元)的测量值来估算主车辆前方道路的算法。我们提出了一种新颖的道路模型,该模型能够以比通常的多项式模型更高的精度描述前方道路。我们还开发了一种贝叶斯融合系统,该系统使用了来自周围环境的以下信息:摄像机和前车的车道标记测量以及雷达-摄像机融合系统的静止物体测量。我们的融合算法的性能在多个路测中得到了评估。不出所料,我们使用的信息越多,性能越好。

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