The automotive world is striving to reduce traffic fatalities to a minimum. As human errors are the main cause of accidents, the industry is pushing advanced driver assistance features. On the route towards autonomous driving, boring routine tasks for the driver will gradually become obsolete. As driver assistance features and navigation systems rely on maps, autonomous driving will need the most recent, most up-to-date maps possible. This becomes clear when investigating the limitations of the range of ego sensors or recognition algorithms as well as information, e.g. legal traffic regulations per country, which cannot be derived from sensor observations.


    Access

    Access via TIB

    Check availability in my library


    Export, share and cite



    Title :

    Sensor-based learning algorithms pave the way towards autonomous driving


    Contributors:

    Conference:

    AmE 2017 – Automotive meets Electronics - 8. GMM-Fachtagung ; 2017 ; Dortmund, Deutschland


    Publication date :

    2017-01-01


    Size :

    6 pages


    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    Towards Fully Autonomous Driving: Systems and Algorithms

    Levinson, J. / Askeland, J. / Becker, J. et al. | British Library Conference Proceedings | 2011


    Towards fully autonomous driving: Systems and algorithms

    Levinson, J. / Askeland, J. / Becker, J. et al. | IEEE | 2011




    FASTrescue deliverable D3.3c : PAVE database

    Olsman, W. F. J. | TIBKAT | 2017