Citation

Rosati Papini GP, Plebe A, Lio MD, Donà R. IEEE Trans. Intel. Transp. Syst. 2022; 23(7): 8805-8822.

Copyright

(Copyright © 2022, IEEE (Institute of Electrical and Electronics Engineers))

DOI

10.1109/TITS.2021.3086397

PMID

unavailable

Abstract

Driving requires the ability to handle unpredictable situations. Since it is not always possible to predict an impending danger, a good driver should preventively assess whether a situation has risks and adopt a safe behavior. Considering, in particular, the possibility of a pedestrian suddenly crossing the road, a prudent driver should limit the traveling speed. We present a work exploiting reinforcement learning to learn a function that specifies the safe speed limit for a given artificial driver agent. The safe speed function acts as a behavioral directive for the agent, thus extending its cognitive abilities. We consider scenarios where the vehicle interacts with a distracted pedestrian that might cross the road in hard-to-predict ways and propose a neural network mapping the pedestrian's context onto the appropriate traveling speed so that the autonomous vehicle can successfully perform emergency braking maneuvers. We discuss the advantages of developing a specialized neural network extension on top of an already functioning autonomous driving system, removing the burden of learning to drive from scratch while focusing on learning safe behavior at a high-level. We demonstrate how the safe speed function can be learned in simulation and then transferred into a real vehicle. We include a statistical analysis of the network's improvements compared to the original autonomous driving system. The code implementing the presented network is available at https://github.com/tonegas/safe-speed-neural-network with MIT license and at https://zenodo.org/communities/dreams4cars.

Language: en

Keywords

autonomous driving; Autonomous vehicles; intelligent speed adaptation; neural networks; Neural networks; reinforcement learning; Reinforcement learning; Roads; Training; Trajectory; transfer learning; Vehicles; Vulnerable road users