A Fuzzy Logic-Based Approach for Humanized Driver Modelling

Abstract

All human drivers can be characterised by their habitual choice of driving behaviours, which results in a wide range of observed driving patterns and manoeuvres. Developing control strategies for autonomous vehicles that address this feature would increase the public acceptance of such vehicles. Therefore, this paper proposes a novel approach to developing rule-based fuzzy logic driver models that simulate different driving styles in the car-following regimes. These driver models were trained with the collected on-road driving data to capture corresponding human drivers’ characteristics. The proposed approach consists of three main components: collecting on-road driving data, developing a vehicle model, and establishing the car-following driver models. Firstly, an instrumented vehicle was used to collect driving data over the same route for three consecutive months. Car-following scenarios during these journeys were extracted, and related data were processed accordingly. Afterwards, a representative model of the instrumented vehicle was created and evaluated. Finally, a fuzzy logic driver model that uses humanized inputs was developed and calibrated with the recorded data. The developed driver model’s performance was assessed using the collected driving data and a baseline PID driver model. With the performance validated, models representing more aggressive and more defensive driving styles were derived following the same procedure. A cross-driver analysis was then implemented in a normalized car-following scenario with the established vehicle model to investigate the impacts of different driving styles further. The developed driver model can introduce driving styles into drive cycle experiments and replicate on-road real driving emission tests in the laboratory. Moreover, as the proposed method has high robustness to incomplete datasets, it can be a more cost-effective option to facilitate the development of humanized and customized vehicle control strategies for autonomous driving.

Intelligent, In-Vehicle Autonomous Decision-Making Functionality for Driving Style Reconfigurations

“…The major implication of this study shows that driver's acceptance and trust towards automated vehicles is enhanced when the in-vehicle designed DS is aligned with driver's DS, and thus, undesired takeover behaviors are reduced. Feng et al [32] proposed a novel data-driven fuzzy logic-based approach, where different driving styles are simulated, by using specified environmental inputs associated with the human driver perception. Extensive evaluation results in a unified comparative environment showcase the differences in DS, as well as their influence on fuel consumption.…”

Heterogeneity in the Driver Behavior: An Exploratory Study Using Real-Time Driving Data

“…e fuzzy rules for classification were predefined based on the driving performance of three expert drivers in terms of speed and longitudinal/ lateral accelerations. Similarly, Feng et al [24] developed a fuzzy logic driver model to simulate different driving styles.…”

Application of CNN-LSTM Model for Vehicle Acceleration Prediction Using Car-following Behavior Data

“…In traditional methods, supervised and unsupervised learning techniques have been commonly used [15]. Supervised learning methods, such as the fuzzy logic algorithm [16,17], have been introduced for segmentation and identifcation. Xie and Zhu [18] employed timestamp-based segmentation and random forest classifcation to analyze driving behavior.…”