A Review of Intelligent Driving Style Analysis Systems and Related Artificial Intelligence Algorithms <s> Driver Distraction Detection <s> Introduction: Research suggests safety climate (SC) is a strong predictor of safety-related outcomes in organizations. This study explores the relationship between six SC dimensions and four aspects of work-related driving. Method: The SC factors measured were "communication and procedures," "work pressures," "relationships," "safety rules," "driver training," and "management commitment." The aspects of self-reported occupational driving measured were traffic violations, driver error, driving while distracted, and pre-trip vehicle maintenance. Results: Hierarchical regression analyses revealed that the SC factors accounted for significant amounts of variance in all four aspects of work-related driving, over and above the control factors of age, sex, and work-related driving exposure. However, further investigation indicated certain SC factors (particularly safety rules, communication, and management commitment) were more strongly related to specific aspects of work-related driving behavior than others. Together, the SC factors were better able to predict self-reported distraction from the road than the other aspects of driving behavior measured. Implications for occupational safety, particularly for the management of work-related drivers are discussed. <s> BIB001 </s> A Review of Intelligent Driving Style Analysis Systems and Related Artificial Intelligence Algorithms <s> Driver Distraction Detection <s> This paper considers a comprehensive and collaborative project to collect large amounts of driving data on the road for use in a wide range of areas of vehicle-related research centered on driving behavior. Unlike previous data collection efforts, the corpora collected here contain both human and vehicle sensor data, together with rich and continuous transcriptions. While most efforts on in-vehicle research are generally focused within individual countries, this effort links a collaborative team from three diverse regions (i.e., Asia, American, and Europe). Details relating to the data collection paradigm, such as sensors, driver information, routes, and transcription protocols, are discussed, and a preliminary analysis of the data across the three data collection sites from the U.S. (Dallas), Japan (Nagoya), and Turkey (Istanbul) is provided. The usability of the corpora has been experimentally verified with a Cohen's kappa coefficient of 0.74 for transcription reliability, as well as being successfully exploited for several in-vehicle applications. Most importantly, the corpora are publicly available for research use and represent one of the first multination efforts to share resources and understand driver characteristics. Future work on distributing the corpora to the wider research community is also discussed. <s> BIB002 </s> A Review of Intelligent Driving Style Analysis Systems and Related Artificial Intelligence Algorithms <s> Driver Distraction Detection <s> This paper presents a non-intrusive approach for monitoring driver drowsiness, based on driver and driving data fusion. The Percentage of Eye Closure (PERCLOS) is used to estimate the driver's state. The PERCLOS is computed on real time using a stereo vision-based system. The driving information used is the lateral position, the steering wheel angle and the heading error provided by the CAN bus. These three signals have been studied in the time and frequency domain. A multilayer perceptron neural network has been trained to fetch an optimal performance score. This system was installed in a naturalistic driving simulator. For evaluation purposes, several experiments were designed by psychologists and carried out with professional drivers. As ground truth, subjective experts' manual annotation of the driver video sequences and driving signals was used. A detection rate of 70% using individual indicators was raised up to 94% with the combination of indicators. An explanation about these results and some conclusion are presented. <s> BIB003
The influence of driver distraction has been investigated in driver simulation research where the driver has to perform secondary tasks while driving, such as solving trivial math problems or repeating alphanumeric strings (ALs) BIB002 , and observations were made that indicate drastic changes in lane-changing behaviour BIB003 , the frequency and delay of checking behaviour (check a side mirror or speedometer), and the shrinking of the visual field of the driver BIB001 . The classification of the driver's state in real-time can effectively be integrated in the IVIS and ADAS technologies by identifying a distracted driver through his driving behaviour, and then communicate the information to the driver, in order to remind the driver to be more attentive to driver actions and surroundings.