Autonomous vehicles and autonomous driving are fascinating topics in the research world and in the car industry today. The benefits foreseen by the introduction of autonomous vehicles are in accordance with the increasingly complex scientific and engineering goals, which are waiting for solutions.

The significant developments in Information and Communication Technologies triggered the era of autonomous vehicles. The achieved and the requested progresses in processors and multiprocessors, sensors, machine learning and especially deep and reinforcement learning, big data and wireless communications represent the bricks from which the autonomous vehicles are and will be built.

My presentation investigates, one of the most challenging aspects of autonomous driving, the problem of environment perception. Perception strategy, disruptive sensors, the sensory system, redundancy, 360 degree coverage, perception solutions at individual sensors level, raw data level fusion versus object level fusion are discussed. The super-sensor concept for spatio-temporal and appearance based intermediate representation (STAR) and the perception algorithms for the surrounding view cameras and LIDARs, the main elements of the perception solution developed at Technical University of Cluj-Napoca are presented.