article · Transportation Engineering
Driver errors are responsible for roughly three quarters of all road traffic collisions. A combined monitoring approach evaluates both vehicle operation and driver state to address these risks. The technique pairs vehicle network data with facial feature tracking using a unit compatible with any vehicle equipped with Controller Area Network technology. Vehicle telemetry, including engine speed, road speed, throttle position, steering angle, engine noise, fuel usage, and exhaust emissions, is tracked to differentiate between normal and aggressive driving. Simultaneously, visual analysis of facial cues identifies signs of driver fatigue and drowsiness. In experimental and simulation tests, this integrated monitoring method achieved an average detection accuracy of 99.10 per cent. By clearly identifying boundaries between safe and unsafe operating styles, the resulting data supports initiatives to boost road safety, inform regulatory policies, and improve driver education programmes.
Driver fatigue and aggressive driving are major contributors to traffic accidents worldwide. Providing an integrated system that reliably identifies dangerous physical states and hazardous vehicle handling in real time helps improve overall road safety. The resulting insights also provide transport authorities and driving schools with clear, empirical data to strengthen road safety policies and refine driver training programmes.
The monitoring unit is designed to integrate with any vehicle supporting Controller Area Network technology, making it applicable to fleet management, commercial transport, and automotive safety systems. Combining on-board vehicle diagnostics with facial tracking software could assist fleet operators in supervising driver alertness and driving style. Having reached the stage of experimental vehicle testing and simulation with high reported accuracy, the technology appears applied and tested, though specific commercial deployment routes are not detailed.
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New research confirms that driving mistakes account for almost 75% of all traffic accidents. Recently, many researchers have studied the recognition of driving behavior, including aggressive, fatigued or drowsy driving. The principal aim of this work is to present a combination approach for evaluating driver behavior, focuses on the analysis of numerous driving-related parameters and the detection of driver fatigue and drowsiness. Experimental research is carried out using a monitoring unit which can be mounted in any vehicle fitted with CAN (Control Area Network) technology, and which monitors current driving actions. This work combines the analysis of driving parameters such as engine speed, vehicle speed, accelerator pedal position, steering wheel angle, engine noise intensity, fuel consumption and, finally, the quantity of exhaust gases generated to define whether driving is normal or aggressive. analysis of the driver's face enables us to detect fatigue and know whether the driver is asleep on the basis of facial features. This proposed detection method achieved an average accuracy of 99.10%. The information gathered for each driving style is recorded, in order to build up our own data and analyze it carefully. In-depth study of these parameters reveals the boundaries that define driving status and style. Experimental and simulation results confirm that the proposed system can effectively detect driving states and driver reactions, with the aim of guaranteeing a sufficient and acceptable level of safety. This study can be used to improve policies and design more robust driver training and education programs.
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DOI: 10.1016/j.treng.2023.100217
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