High-Level Data Fusion Based Probabilistic Situation Assessment for Highly Automated Driving

A primary challenge of automated driving systems is the task of a situation assessment. This paper presents a high-level data fusion based probabilistic situation assessment method which is capable of assessing a current traffic situation and giving a recommendation about driving behaviors. The proposed method consists of two steps: high-level data fusion and probabilistic situation assessment. The high-level data fusion, designed to provide a better understating of observed situations, produces a local dynamic road map by integrating all dynamic entities with a high-precision static road map. The probabilistic situation assessment estimates threat levels of each lane as the probability of the lane state through the use of independent local experts based on the local dynamic road map. The recommendations for behavior decision are determined by filtering out noises resulting from object tracking even though a tracking module misses objects or detects wrong objects a lot, but immediately. The method is implemented in an open-source robot operating system to provide a reusable and hardware independent software platform, and verified and evaluated through in-vehicle tests on real highways in real-time operation.