Enhanced obstacle detection based on Data Fusion for ADAS applications

Fusion is a common topic nowadays in Advanced Driver Assistance Systems (ADAS). The demanding requirements of safety applications require trustable sensing technologies. Fusion allows to provide trustable detections by combining different sensor devices, fulfilling the requirements of safety applications. High level fusion scheme is presented; able to improve classic ADAS systems by combining different sensing technologies i.e. laser scanner and computer vision. By means of powerful Data Fusion (DF) algorithms, the performance of classic ADAS detection systems is enhanced. Fusion is performed in a decentralized scheme (high level), allowing scalability; hence new sensing technologies can easily be added to increase the trustability and the accuracy of the overall system. Present work focus in the Data Fusion scheme used to combine the information of the sensors at high level. Although for completeness some details of the different detection algorithms (low level) of the different sensors is provided. The proposed work adapts a powerful Data Association technique for Multiple Targets Tracking (MTT): Joint Probabilistic Data Association (JPDA) to improve the trustability of the ADAS detection systems. The final application provides real time detection of road users (pedestrians and vehicles) in real road situations. The tests performed proved the improvement of the use of Data Fusion algorithms. Furthermore, comparison with other classic algorithms such as Global Nearest Neighbors (GNN) proved the performance of the overall architecture.