ANNAPOLIS: AutoNomous Navigation Among Personal mObiLity devIceS
Abstract: Urban centers are increasingly invaded by new means of PLEV (Personal Light Electric Vehicles: electric scooters, Hoverboards, Gyro-wheels, etc.), directly or indirectly at the source of unpredictable behaviors in the traffic environment. The “Mobility Law 2019” bill provides for the return of the scooters to the traffic lane when the dedicated bicycle lanes do not exist. In such a context, autonomous vehicles suffer from their limited perception obtained only from on-board sensors (forced to undergo the movements of the vehicle) and sometimes reduced in the measurement field by bulky obstacles (buses, trucks, etc.) or an occluding environment (buildings or urban structures). In such situation, unforeseen and unexpected events take source from the presence of new electrical mobility systems, or from behaviors of unstable pedestrians using (or not) new PLEV and respecting (or not) the traffic rules. ANNAPOLIS will increase the vehicle’s perception capacity both in terms of precision, measurement field of view and information semantics, through vehicle to intelligent infrastructure communication. The project will also seek new models or concepts to take into account unpredictable behaviors of the new means of individual electric transport, to interpret and analyze scenes under constant evolution, and finally to decide the best future and safe motion of the self-driving car even in highly dynamic environments with unexpected and dangerous events.
Keywords: Safety and security, ADAS, autonomous vehicle, PLEV, connected vehicle, smart cities, intelligent transport systems, artificial intelligence, decision making under uncertainty.
Definition: In ANNAPOLIS, unforeseen and unexpected events take source from situations where the perception space is hidden by bulky road obstacles (e.g. truck/bus) or an occluding environment, from the presence of new Personal Light Electric Vehicles (PLEV), or from erratic behaviors of unstable pedestrians using (or not) new electrical mobility systems and respecting (or not) the traffic rules.
Objectives
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Objectives 1: The vehicle perception capacity of the vehicle will be augmented by fusionning abstracted information coming from connected Road Side Units.
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Objectives 2: Unforeseen, unexpected and risky situations will be detected and interpreted as attention maps by using both data driven and model based approaches.
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Objectives 3: Robust, safe and smooth motion will be computed by using a Bayesian Decision Network and validated by a risk management module.
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Objectives 4: Dedicated annotated datasets on PLEV will be built and used for training and validation.
