Data-driven neural network model for maximum speed profile based on driver risk

Abstract

In the field of vehicle control systems, the primary objective is to ensure the safety of road users. The intricate dynamics of road vehicles necessitate a high level of precision. Vehicle safety encompasses a multitude of considerations, including vehicle trajectory, prevailing traffic conditions, road structure, and meteorological factors. This study employs an Artificial Neural Network (ANN) trained with human driver data using SCANeR Studio software to evaluate the risk for the driver. The risk has been defined as a five-level parameter, which depends on the potential danger of a situation, where speed and direction play a crucial role. The system incorporates a simulator, an ANN, and a display interface to present the surroundings and communicate important information to the driver. This research employs a simulated driving scenario comprising a multi-lane roundabout with vehicles travelling in different directions to simulate real-world challenges. Risk estimation is achieved through a Time Delay Neural Network (TDNN) trained with various information about the environment in relation to the driven vehicle. The research employs a Jackknife technique for overall evaluation and introduces an adaptive algorithm for speed limit setting. The findings demonstrate the stability, generality, and practical applicability of the ANN in enhancing road safety.