Skills and Cooperation
« One of the major problems for autonomous vehicles is the ability to detect VRU (Vunerable Road Users = pedestrians, cyclists, scooters) and this in all visibility conditions (including night, headlight glare, tunnel entrances/exits, smoke, fog, etc...). The current systems mainly use visible cameras which are in difficulty or even inoperative in these situations. Thermal infrared cameras can address these difficult situations with great efficiency. There remains the problem of data fusion: how to optimize the pedestrian detection function by making the most of each sensor (visible + infrared)? We called upon Neovision, which was able to take charge of all the phases of the project: state of the art of the possible fusion modes, prototyping of the most promising architectures, constitution of a consequent database (driving with recording of approximately 1M images on 2 visible cameras and 2 IR cameras), training, optimization of the performances, tests, and finally integration in a live demonstrator which had to function in real time. In the end, it is a neural network that outperforms classical architectures by improving performance in all situations, a co-authored publication, and a working real-time demonstrator! Dealing with a complex problem like this can only work if the teams are competent and if they cooperate. This is the last point I would like to emphasize: it is also thanks to the good cooperation of the Neovision teams with our teams that we were able to achieve such results. »