Using probabilistic hierarchical road perception and online system performance assessment.
One of the biggest challenges towards fully automated driving is
achieving robustness. Autonomous vehicles will have to fully recognize
their environment even in harsh weather conditions. Additionally, they have to be able to detect sensor and algorithm failures and react properly to keep the vehicle in a safe state. These two challenges are addressed exemplarily on miniature cars. We extend the approach of Compositional Hierarchical Models by temporal fusion to achieve a robust environment perception. The increased association problem is overcome by a grid-based approximation and a voting system. System performance assessment surveils the system’s performance and reacts with driving function degradation or activation of specialized algorithms. The approach was evaluated at the final of the Audi Autonomous Driving Cup 2016. This video shows the advanced driving capabilities under harsh environment conditions and the source code is available for download.
Paper publication and source code is available at url.fzi.de/aadc.
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