AI Article Synopsis

  • Road accidents in Malaysia are rising annually, and collecting reliable pre-crash data has proven difficult due to biases in existing research methods like simulators and surveys.
  • This study introduces Malaysia's first naturalistic driving study, using 30 drivers and advanced sensors to gather continuous driving data over 750 km.
  • Findings reveal significant differences in driving behavior based on gender, age, cultural background, and driving times, leading to recommendations for enhancing traffic safety and preventing future accidents.

Article Abstract

Road accidents are increasing every year in Malaysia, and it is always challenging to collect reliable pre-crash data in the transportation community. Existing studies relied on simulators, police crash reports, questionnaires, and surveys to study Malaysia's drivers' behavior. Researchers previously criticized such methods for being biased and unreliable. To fill in the literature gap, this study presents the first naturalistic driving study in Malaysia. Thirty drivers were recruited to drive an instrumented vehicle for 750 km while collecting continuous driving data. The data acquisition system consists of various sensors such as OBDII, lidar, ultrasonic sensors, IMU, and GPS. Irrelevant data were filtered, and experts helped identify safety criteria regarding multiple driving metrics such as maximum acceptable speed limits, safe accelerations, safe decelerations, acceptable distances to vehicles ahead, and safe steering behavior. These thresholds were used to investigate the influence of social and cultural factors on driving in Malaysia. The findings show statistically significant differences between drivers based on gender, age, and cultural background. There are also significant differences in the results for those who drove on weekends rather than weekdays. The study presents several recommendations to various public and governmental sectors to help prevent future accidents and improve traffic safety.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8619293PMC
http://dx.doi.org/10.3390/ijerph182211740DOI Listing

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