Abstract
Obstacle detection is a widely studied field in the automotive industry because of the great importance it assumes in all systems that provide autonomous navigation of vehicles in an environment.
Many different obstacle detection systems have been developed. The main differences between these systems are the types of algorithms and sensors employed. Many studies have focused on road obstacle detection in order to perform such tasks as pre-crash, collision mitigation, stop and go, obstacle avoidance, and inter-distance management. An important issue in ensuring the reliability of obstacle detection is the choice of sensors: digital cameras, infrared sensors, laser scanners, radar, and sonar are commonly used to provide a complete representation of the vehicle’s surrounding area, allowing interaction with the world. Inertial sensors like odometers, speed sensors, position sensors, accelerometers, and tilt sensors are used to monitor the motion of the vehicle, measuring its speed, orientation, and position. This chapter is structured in three main sections: The first will introduce a classification of all perception sensors that can be used in this field; a brief description of each sensor will be provided in order to underline pros and cons regarding the obstacle detection field. In the second section, the main algorithms of obstacle detection will be shown, classified by the kind of sensor (or sensors) employed. The third section presents obstacle detection systems that use sensory fusion combining artificial vision with distance detection sensors like laser or radar.
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Debattisti, S. (2012). Perception Tasks: Obstacle Detection. In: Eskandarian, A. (eds) Handbook of Intelligent Vehicles. Springer, London. https://doi.org/10.1007/978-0-85729-085-4_41
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DOI: https://doi.org/10.1007/978-0-85729-085-4_41
Publisher Name: Springer, London
Print ISBN: 978-0-85729-084-7
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