Development of Localization and Planning Algorithms for Autonomous Driving Based on Public Map Databases
The development of safe and reliable Automated Driving Systems (ADSs) represent one of the most significant challenges in the field of automotive. ; Thanks to advancements in sensors technology, and artificial intelligence, particularly Deep Learning, ADSs are becoming increasingly more sophisticated and complex. ; The widespread adoption of Autonomous Vehicles (AVs) on public roads present several potential benefits. AV technology promise to improve safety, being human error one of the main factors in the majority of traffic accidents. AVs can also make car-sharing services more efficient, reducing the number of vehicles circulating on urban roads. This would decrease the need for parking spaces and contribute to a decrease in greenhouse emissions. ; ; Despite the potential, several challenges still limit the deployment of ADSs. ; One of the main factors is represented by costs, as AVs are still much more expensive than conventional vehicles. ; ; In addition, deploying AVs in new environments is a resource intensive and lenghty process, as most state-of-the-art autonomous driving algorithms rely on a dataset of custom pre-existing information. ; ; The research work aims at improving scalability of AV systems by proposing novel localization and planning algorithms. ; The proposed methods leverage publicly available databases to replace reliance on a-priori information. ; These solutions include the definition of a localization approach that does not require exploration of the environment prior to navigation. ; In addition, we propose a lateral planning method that does not rely on a high-definition map of the environment structure. ; Finally, we propose a method for intersection management, exploiting public maps for predicting the behaviour of other vehicles. ; The algorithms developed during the research activity have all been validated on an AV platform driving on public urban roads.
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