The Role of Multisensor Environmental Perception for Automated Driving
In order to facilitate automated driving, a reliable representation of a vehicle’s environment is required. This chapter provides a survey of techniques for the perception of both static and dynamic environments including key algorithms for object tracking and data fusion. In addition, the particular challenges of this field from a practitioner’s perspective are discussed and compared to the state-of-the-art design and implementation paradigms.
KeywordsPerception Data fusion Object tracking Occupancy grids
- 2.R. Schubert et al., Empirical evaluation of vehicular models for ego motion estimation, in Intelligent Vehicles Symposium (IV), 2011 IEEE. IEEE (2011)Google Scholar
- 5.D. Musicki, R. Evans, Joint Integrated Probabilistic Data Association—JIPDA, in Information Fusion, 2002. Proceedings of the Fifth International Conference on, vol. 2, pp. 1120–1125, 8–11 Jul 2002Google Scholar
- 6.C. Adam, R. Schubert, G. Wanielik, Radar-based extended object tracking under clutter using generalized probabilistic data association, in Intelligent Transportation Systems—(ITSC), 2013 16th International IEEE Conference on, pp. 1408–1415, 6–9 Oct 2013. doi: 10.1109/ICIF.2002.1020938
- 9.M.M. Muntzinger et al., Reliable automotive pre-crash system with out-of-sequence measurement processing, in Intelligent Vehicles Symposium (IV), 2010 IEEE. IEEE (2010)Google Scholar
- 10.A. Rauch et al., Car2x-based perception in a high-level fusion architecture for cooperative perception systems. Intelligent Vehicles Symposium (IV), 2012 IEEE. IEEE (2012)Google Scholar