Literature DB >> 26436229

Driver Behavior During Overtaking Maneuvers from the 100-Car Naturalistic Driving Study.

Rong Chen1, Kristofer D Kusano1, Hampton C Gabler1.   

Abstract

OBJECTIVE: Lane changes with the intention to overtake the vehicle in front are especially challenging scenarios for forward collision warning (FCW) designs. These overtaking maneuvers can occur at high relative vehicle speeds and often involve no brake and/or turn signal application. Therefore, overtaking presents the potential of erroneously triggering the FCW. A better understanding of driver behavior during lane change events can improve designs of this human-machine interface and increase driver acceptance of FCW. The objective of this study was to aid FCW design by characterizing driver behavior during lane change events using naturalistic driving study data.
METHODS: The analysis was based on data from the 100-Car Naturalistic Driving Study, collected by the Virginia Tech Transportation Institute. The 100-Car study contains approximately 1.2 million vehicle miles of driving and 43,000 h of data collected from 108 primary drivers. In order to identify overtaking maneuvers from a large sample of driving data, an algorithm to automatically identify overtaking events was developed. The lead vehicle and minimum time to collision (TTC) at the start of lane change events was identified using radar processing techniques developed in a previous study. The lane change identification algorithm was validated against video analysis, which manually identified 1,425 lane change events from approximately 126 full trips.
RESULTS: Forty-five drivers with valid time series data were selected from the 100-Car study. From the sample of drivers, our algorithm identified 326,238 lane change events. A total of 90,639 lane change events were found to involve a closing lead vehicle. Lane change events were evenly distributed between left side and right side lane changes. The characterization of lane change frequency and minimum TTC was divided into 10 mph speed bins for vehicle travel speeds between 10 and 90 mph. For all lane change events with a closing lead vehicle, the results showed that drivers change lanes most frequently in the 40-50 mph speed range. Minimum TTC was found to increase with travel speed. The variability in minimum TTC between drivers also increased with travel speed.
CONCLUSIONS: This study developed and validated an algorithm to detect lane change events in the 100-Car Naturalistic Driving Study and characterized lane change events in the database. The characterization of driver behavior in lane change events showed that driver lane change frequency and minimum TTC vary with travel speed. The characterization of overtaking maneuvers from this study will aid in improving the overall effectiveness of FCW systems by providing active safety system designers with further understanding of driver action in overtaking maneuvers, thereby increasing system warning accuracy, reducing erroneous warnings, and improving driver acceptance.

Entities:  

Keywords:  100-Car; driver behavior; forward collision warning; lane change; rear impact

Mesh:

Year:  2015        PMID: 26436229     DOI: 10.1080/15389588.2015.1057281

Source DB:  PubMed          Journal:  Traffic Inj Prev        ISSN: 1538-9588            Impact factor:   1.491


  3 in total

1.  Risky Driving Behavior Recognition Based on Vehicle Trajectory.

Authors:  Shengdi Chen; Qingwen Xue; Xiaochen Zhao; Yingying Xing; Jian John Lu
Journal:  Int J Environ Res Public Health       Date:  2021-11-24       Impact factor: 3.390

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

Authors:  Ward Ahmed Al-Hussein; Lip Yee Por; Miss Laiha Mat Kiah; Bilal Bahaa Zaidan
Journal:  Int J Environ Res Public Health       Date:  2022-01-27       Impact factor: 3.390

3.  Investigating the Effect of Social and Cultural Factors on Drivers in Malaysia: A Naturalistic Driving Study.

Authors:  Ward Ahmed Al-Hussein; Miss Laiha Mat Kiah; Lip Yee Por; Bilal Bahaa Zaidan
Journal:  Int J Environ Res Public Health       Date:  2021-11-09       Impact factor: 3.390

  3 in total

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