Educational driving through intelligent traffic simulation

Abstract

Modelling driving dynamics in experimental educational scenarios represents a key enhancement of a SMART city, where citizen-oriented politics promote traffic rules knowledge retention and awareness. We propose an instructional design of mapping simulations of real-world urban networks as use cases for practicing traffic rules. Traffic simulations have been implemented through Reinforcement Learning agents, using a modified Policy Proximal Optimization (PPO) strategy, demonstrating a good sample efficiency. The proposed objective function and the selected policy positive and negative rewards empower the car agent to reach a predefined destination, from a predefined start position, while adapting to the route line. Results validate the applicability of the proposed approach to educational simulations, within a generic gamified environment. The approach proposes a further extension towards adaption to complex lane design (e.g. traffic signs) and player’s in-game behavior.