A Review of Intelligent Driving Style Analysis Systems and Related Artificial Intelligence Algorithms

Abstract

In this paper the various driving style analysis solutions are investigated. An in-depth investigation is performed to identify the relevant machine learning and artificial intelligence algorithms utilised in current driver behaviour and driving style analysis systems. This review therefore serves as a trove of information, and will inform the specialist and the student regarding the current state of the art in driver style analysis systems, the application of these systems and the underlying artificial intelligence algorithms applied to these applications. The aim of the investigation is to evaluate the possibilities for unique driver identification utilizing the approaches identified in other driver behaviour studies. It was found that Fuzzy Logic inference systems, Hidden Markov Models and Support Vector Machines consist of promising capabilities to address unique driver identification algorithms if model complexity can be reduced.

Enhancing driving safety: Discovering individualized hazardous driving scenes using GIS and mobile sensing

“…A smartphone is placed horizontally in a vehicle with its axes aligned with the vehicle's axes in the current version, which will be improved to achieve a constraint‐free smartphone placement in future work. The accelerometer measures the vehicle's acceleration (m/s 2 ) in a three‐axis frame, which can effectively capture the “jerk energy.” The gyroscope detects the vehicle's rotation (°/s) and aids in inferring turning events and lane changes (Hong et al, ; Júnior et al, ; Meiring & Myburgh, ). The smartphone built‐in GPS data is adopted to record driving trajectories and geotag‐detected driving mistakes, as detailed in Section 2.1.3.…”

“…Machine learning analysis has also been tested to assess driving behaviors (Hong, Margines, & Dey, 2014;Júnior et al, 2017;Meiring & Myburgh, 2015). Júnior et al (2017) attempted to identify the best combinations of mobile sensors and machine learning algorithms (MLAs) to recognize aggressive driving behaviors.…”

“…The accelerometer measures the vehicle's acceleration (m/s 2 ) in a three-axis frame, which can effectively capture the "jerk energy." The gyroscope detects the vehicle's rotation (°/s) and aids in inferring turning events and lane changes (Hong et al, 2014;Júnior et al, 2017;Meiring & Myburgh, 2015). The smartphone built-in GPS data is adopted to record driving trajectories and geotag-detected driving mistakes, as detailed in Section 2.1.3.…”

A Choquet Fuzzy Integral Vertical Bagging Classifier for Mobile Telematics Data Analysis

“…Mobile telematics is a new technology, but driving style behavioral data analytics is not new. Meiring and Myburgh [7] provided a literature review of different driving styles. They categorized driving styles into four main groups: safe, aggressive, inattentive, and drunk.…”

Identifying Driver Behaviors Using Trajectory Features for Vehicle Navigation

“…Prior work in transportation research [2,1] often characterizes drivers using their levels of aggressiveness and carefulness. Several works in modeling pedestrian trajectories [3] and navigation [4] algorithms have applied psychological theory to capture human behavior.…”