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A comprehensive examination of naturalistic lane-changes
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TLDR
In this paper, the authors provided valuable insight into the nature and severity of lane changes in a naturalistic driving environment and provided recommendations for designers of a lane change collision avoidance system in terms of display location and activation criteria.Abstract:
This research effort provided valuable insight into the nature and severity of lane changes in a naturalistic driving environment. Sixteen commuters who normally drove more than 25 miles (40 km) in each direction participated. The two research vehicles were a sedan and a sport utility vehicle; each participant drove each vehicle for ten days. Data gathering was automatic, and no experimenter was present in the vehicle. There were 8,667 lane changes observed over 23,949 miles of driving, making this the largest known data collection effort for the study of lane changes. Analysis of the full data set resulted in many interesting findings regarding the frequency, duration, urgency, and severity of lane changes in regard to maneuver type, direction, and other classification variables. A subset of the full data set (500 lane changes) was then analyzed in greater depth using the sensor data collected by the instrumented vehicle. The sampled lane changes were generally of the more severe and urgent types since these are the cases in which a lane change collision avoidance system (CAS) is likely to be of greatest help. Variables analyzed for the sampled lane changes included turn signal use, braking behavior, steering behavior, eye glance patterns, and forward and rearward area analysis. The concept of a safety envelope for lane changes was then developed using the forward and rearward area analyses. Finally, the data were used to provide recommendations for designers of a lane change CAS in terms of display location and activation criteria. Overall, the research described in this report provides insight into the behaviors and parameters associated with lane changes, while the naturalistic data archive has the potential to address other questions related to driving behavior.read more
Citations
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The Impact of Driver Inattention on Near-Crash/Crash Risk: An Analysis Using the 100-Car Naturalistic Driving Study Data
TL;DR: In this article, the authors presented a study on the safety of self-driving cars with the National Highway Traffic Safety Administration (NHTSA) and the U.S. Office of Human-Vehicle Performance Research.
Journal ArticleDOI
Accelerated Evaluation of Automated Vehicles Safety in Lane-Change Scenarios Based on Importance Sampling Techniques
Ding Zhao,Henry Lam,Huei Peng,Shan Bao,David J. LeBlanc,Kazutoshi Nobukawa,Christopher S. Pan +6 more
TL;DR: Frontal collision due to unsafe cut-ins is the target crash type of this paper and the cross-entropy method is used to recursively search for the optimal skewing parameters to accelerate the verification of AVs in simulations and controlled experiments.
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On the Roles of Eye Gaze and Head Dynamics in Predicting Driver's Intent to Change Lanes
Anup Doshi,Mohan M. Trivedi +1 more
TL;DR: Quantitative results from a naturalistic driving study show that head motion, when combined with lane position and vehicle dynamics, is a reliable cue for lane-change intent prediction, and there may be a biological basis for head motion to begin earlier than eye motion during "lane-change"-related gaze shifts.
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Modeling Duration of Lane Changes
Tomer Toledo,David Zohar +1 more
TL;DR: Models of the duration of lane changes are presented using detailed vehicle trajectory data collected in naturalistic driving with high-mounted video cameras for passenger cars and heavy vehicles and statistical tests are conducted for the similarity between the lane-change durations of the two vehicle types.
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Personalized Driver/Vehicle Lane Change Models for ADAS
Vadim Butakov,Petros Ioannou +1 more
TL;DR: A methodology that learns the characteristics of an individual driver/vehicle response before and during lane changes and under different driving environments is developed and can be used as a kernel component of ADAS to provide more personalized recommendations to the driver, increasing the potential for more widespread acceptance and use ofADAS.
References
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