Understanding Driving Behaviours: Classification and Regression Approaches

Abstract

The emergence of self-driving technology is poised to revolutionize transportation by empowering vehicles to operate autonomously. These advancements are classified into distinct levels of automation, delineating the progression from rudimentary driver assistance to complete autonomy. However, these advancements are accompanied by a spectrum of challenges. For example, the intricate interplay between autonomous and human-driven vehicles necessitates intuitive human interaction capabilities. This thesis focuses on driver behaviour learning, which is a way towards a safe and personal self-driving system. The eventual goal of driver behaviour learning is to equip self-driving vehicles with a deep understanding of human driving patterns. Leveraging learned human behaviours, self-driving vehicles can mirror familiar and intuitive driving actions, thereby fostering harmonious and predictable engagements on the road.

This thesis focuses on investigating using different machine learning methods to learn an accurate and robust predictor of driver behaviours from multiple drivers and also personalize outputs for each individual driver. We define three driving behaviours. 1. Driver Identification: Given an observed driving execution history, identify the driver in the vehicle. 2. Lane Positioning: Given an observed driving execution history, predict the subject vehicle in the lane in the foreseeable future. 3. Lane Keeping: Given an observed driving execution history, predict the speed and steering angle of the subject vehicle in the foreseeable future.

In the experiment, we mainly test with LSTM and Transformer, which are commonly used in time-series data. Specifically, we conduct experiments with LSTM and Transformer encoders to examine their capacity to model driving data in three behaviours. Furthermore, we employ LSTM and Transformer decoders to forecast lane positions, speeds, and angles. Variations in hyperparameter settings, such as width and depth, are investigated for all three behaviors in search of potential optimal configurations.