Driver aggressiveness detection via multisensory data fusion

Abstract

Detection of driver aggressiveness is a significant method in terms of safe driving. Every year, a vast number of traffic accidents occur due to aggressive driving behaviour. These traffic accidents cause fatalities, severe disorders and huge economical cost. Therefore, detection of driver aggressiveness could help in reducing the number of traffic accidents by warning related authorities to take necessary precautions. In this work, a novel method is introduced in order to detect driver aggressiveness on vehicle. The proposed method is based on the fusion of visual and sensor features to characterize related driving session and to decide whether the session involves aggressive driving behaviour. Visual information is used to detect road lines and vehicle images, whereas sensor information provides data such as vehicle speed and engine speed. Both information is used to obtain feature vectors which represent a driving session. These feature vectors are obtained by modelling time series data by Gaussian distributions. An SVM classifier is utilized to classify the feature vectors in order for aggressiveness decision. The proposed system is tested by real traffic data, and it achieved an aggressive driving detection rate of 93.1 %.

Fast and robust approaches for lane detection using multi‐camera fusion in complex scenes

“…The road information acquired by the top‐view cameras is used to supplement the lane information collected by the front‐view camera. We use integrated multi‐source information detection for lane detection [45, 46], so as to obtain the position of the vehicle relative to the lane in the road environment. Hence, the missed detection and false detection in congestion and other traffic conditions can be reduced, which will be verified and analysed in the next experimental section.…”

Vehicular Data Space: The Data Point of View

“…Such data were retrieved using a Raspberry Pi device connected to the CAN through an OBD port. Kumtepe et al [42] developed a solution to detect the driver's aggressiveness in a vehicle using visual information and in-vehicle sensor data acquired from the CAN, such as vehicle speed and engine rotation (RPM). They could detect aggressive driving behavior with a success rate of over 93%.…”

“…Relying on visual information, Kumtepe et al [42] developed a method to detect the driver's aggressiveness by detecting lane deviation and collision time. Andrieu and Pierre [66] employed a GPS, front car camera and a fuel flow meter to develop an efficient EDAS.…”

Real-Time Video Processing using Contour Numbers and Angles for Non-urban Road Marker Classification

“…In earlier works, many efforts were invested in solving lane detection and tracking problems for Auto-Assist Driving System (ADS) [2], [3]. These methods had been used as a warning system to the driver [4]- [8] and surprisingly, the lane detection also was used as part of the system to analyse the driver behaviour [9]. Symbols, alphabets, and custom markers were also being explored and used as the information to alert drivers [10,11].…”