Real-time object recognition on image sequences with the adaptable time delay neural network algorithm — applications for autonomous vehicles

摘要

Within the framework of the vision-based “Intelligent Stop&Go” driver assistance system for both the motorway and the inner city environment, we present a system for segmentation-free detection of overtaking vehicles and estimation of ego-position on motorways as well as a system for the recognition of pedestrians in the inner city traffic scenario. Both systems are running in real-time in the test vehicle UTA of the DaimlerChrysler computer vision lab, relying on the adaptable time delay neural network (ATDNN) algorithm. For object recognition, this neural network processes complete image sequences at a time instead of single images, as it is the case in most conventional neural algorithms. The results are promising in that using the ATDNN algorithm, we are able to perform the described recognition tasks in a large variety of real-world scenarios in a computationally highly efficient and rather robust and reliable manner.