Driver Behavior Profiling and Recognition Using Deep-Learning Methods: In Accordance with Traffic Regulations and Experts Guidelines

Abstract

The process of collecting driving data and using a computational model to generate a safety score for the driver is known as driver behavior profiling. Existing driver profiles attempt to categorize drivers as either safe or aggressive, which some experts say is not practical. This is due to the “safe/aggressive” categorization being a state that describes a driver’s conduct at a specific point in time rather than a continuous state or a human trait. Furthermore, due to the disparity in traffic laws and regulations between countries, what is considered aggressive behavior in one place may differ from what is considered aggressive behavior in another. As a result, adopting existing profiles is not ideal. The authors provide a unique approach to driver behavior profiling based on timeframe data segmentation. The profiling procedure consists of two main parts: row labeling and segment labeling. Row labeling assigns a safety score to each second of driving data based on criteria developed with the help of Malaysian traffic safety experts. Then, rows are accumulated to form timeframe segments. In segment labeling, generated timeframe segments are assigned a safety score using a set of criteria. The score assigned to the generated timeframe segment reflects the driver’s behavior during that time period. Following that, the study adopts three deep-learning-based algorithms, namely, Deep Neural Network (DNN), Recurrent Neural Network (RNN), and Convolutional Neural Network (CNN), to classify recorded driving data according to the established profiling procedure, and selects the most suitable one for a proposed recognition system. Various techniques were used to prevent the classification algorithms from overfitting. Using gathered naturalistic data, the validity of the modulated algorithms was assessed on various timeframe segments ranging from 1 to 10 s. Results showed that the CNN, which achieved an accuracy of 96.1%, outperformed the other two classification algorithms and was therefore recommended for the recognition system. In addition, recommendations were outlined on how the recognition system would assist in improving traffic safety.

Driver Profile and Driving Pattern Recognition for Road Safety Assessment: Main Challenges and Future Directions

“…Data from naturalistic driving experiments are also used from [58], [59], [60], [61], [62] to discover the existing driving patterns. The number of participants ranges from 16 to 89 in these studies and the mostly used driving metrics were speed, acceleration and braking, as in the rest of the studies, followed by RPM and yaw/pitch/roll.…”

Investigating the Effect of COVID-19 on Driver Behavior and Road Safety: A Naturalistic Driving Study in Malaysia

“…Researchers and experts determined that these factors were the most relevant in predicting the aggressive behavior of Malaysian drivers. The criteria for determining safe and aggressive driving in terms of speeding, distancing, acceleration, deceleration, and steering are shown in Table 1 as reported in [ 17 , 22 ].…”

“…Due to the fact that the OSeven platform follows strict information security procedures and privacy policies, all data are delivered in an anonymized manner, making it difficult to determine how individuals’ driving behaviors changed during the COVID-19 pandemic. Self-reported data have long been criticized in the scientific literature for being biased and less accurate than naturalistic driving data (NDD) [ 16 , 17 , 18 ]. Furthermore, the effect of COVID-19 on transportation can be assessed through the reports of individual academic institutions such as ETZ Zurich [ 19 ] or data companies such as Google [ 20 ], which have published summaries of COVID-19-related activity statistics.…”

An Efficient Deep Learning Model Based on Driver Behaviour Detection Within CAN-BUS Signals

“…Such Convolutional Neural Networks are deep learning algorithms. (CNN) [30][31][32][33][34], Recurrent Neural Network (RNN), clustering, etc. Have been put into practice for numerous driver-style applications [35].…”