01625588

Component

https://doi.org/10.3141/2663-17

Most research in vehicle automation has mainly focused on the safety aspect with only limited studies focusing on occupants’ discomfort. To facilitate their rapid uptake and penetration, autonomous vehicles (AVs) should ensure that occupants are both safe and comfortable. Recent research, however, revealed that people felt uncomfortable when AVs braked. This may be caused by the robotlike braking performance. Existing studies on drivers’ braking behavior investigated data from either controlled experiments or driving simulators. There is a dearth of research on braking behavior in normal driving. The objective of the study was to examine drivers’ braking behaviors by exploiting naturalistic driving data from the Pan-European TeleFOT project (Field Operational Tests of Aftermarket and Nomadic Devices in Vehicles). On a fixed route of 16.5 km, 16 drivers were asked to drive an instrumented vehicle. About 11 million observations were analyzed to identify the profile, value, and duration of deceleration events. Since deceleration events are nested within trips and trips within drivers, multilevel mixed-effects linear models were employed to develop relationships between deceleration value and duration and the factors influencing them. Results indicate that the most-used profile of the deceleration behavior follows a hard braking at the beginning when detecting a danger and then becomes smoother. Furthermore, the results suggest that the speed, reason for braking, and deceleration profile mostly affect the deceleration events. Findings from this study should be considered in examining the braking behavior of AVs.

01653375

17-03255

English

pp 134–141

2017

Transportation Research Record: Journal of the Transportation Research Board

9780309442060

Digital/other

Figures (4) ; Maps; Photos; References (22) ; Tables (2)

Automatic data collection systems; Autonomous land vehicles; Behavior; Braking; Deceleration; Drivers

Highways; Safety and Human Factors

TRIS, TRB, ATRI

Dec 8 2016 11:13AM

• Age-Based Simulation of Merging Behavior at Freeway Merging Ramps
• Automatic Driver Head State Estimation in Challenging Naturalistic Driving Videos
• Comparative Analysis of Toll Plaza Safety Features in Puerto Rico and Massachusetts with a Driving Simulator
• Crash Heterogeneity: Implications for Naturalistic Driving Studies and for Understanding Crash Risks
• Drivers’ Lane-Keeping Ability in Heavy Rain: Preliminary Investigation Using SHRP 2 Naturalistic Driving Study Data
• Effects of a Change in Environment on the Minimum Time to Situation Awareness in Transfer of Control Scenarios
• Effects of Alert Cue Specificity on Situation Awareness in Transfer of Control in Level 3 Automation
• Examining Potential Confounds in Use of the Detection Response Task for In-Vehicle System Evaluation
• How to Drive Drivers Nuts: Effect of Auditory, Vibrotactile, and Multimodal Warnings on Perceived Urgency, Annoyance, and Acceptability
• Investigating Driver Behavior at Minor-Street Stop-Controlled Intersections in Qatar
• Linking the Detection Response Task and the AttenD Algorithm Through Assessment of Human–Machine Interface Workload
• Modeling Effects of Forward Glance Durations on Latent Hazard Detection
• Modeling of Vehicles Merging Movement at Unsignalized Intersections Considering Drivers’ Risk Perception
• On-Road Visual Sign Salience, Driver Attention Allocation, and Target Detection Accuracy
• Simulator Study of Involuntary Driver Distraction Under Different Perceptual Loads
• Strategies to Assist Drivers in Remaining Attentive While Under Partially Automated Driving: Verification of Human–Machine Interface Concepts