Embedded sensor fusion deep network for road user detection

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During the past ten years, tremendous progress has been achieved in the fields of artificial intelligence, sensor technology, low-power processing units and connectivity. As a result, self-driving vehicles emerge as very promising and fruitful technology attracting a large number of technology giants, startups, as well as researches from all aroundthe world. An autonomous vehicle is one that incorporates a fully automated driving system, that can drive and navigate on the road by itself, without requiring any human intervention, and can respond to environmental situations. Perception is a critical module in any automated driving system. In this research project, we address the problem of the detection and localization of the road users (vehicles, pedestrians, cyclists, etc.) surrounding the driving environment of an intelligent vehicle.Intelligent vehicles are generally equipped with three main sensor types: cameras, lidar (or laser scanner) and radar. All these sensors are intrinsically very complementary and the strength of one can compensate for the weakness of the other. However, under challenging environmental conditions, such as adverse weather, the operationality of a given sensor might be severely impacted and the detection results may then become unreliable. To solve this issue, the performance of a perception system can be significantly improved by combining the information coming from different modalities to overcome the shortcomings of individual sensors. This research project will focus on the design, deployment, optimization and embedding of a convolutional neural network (CNN) for sensor fusion in the context of advanced driver assistance systems and autonomous vehicles.