Typical Acceleration and Speed Profiles for Right-Turn Maneuvers Based on SHRP2 Naturalistic Driving Data

2024-01-2472

04/09/2024

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Event
WCX SAE World Congress Experience
Authors Abstract
Content
The goal of this study was to use naturalistic driving data to characterize the motion of vehicles making right turns at signalized intersections. Right-turn maneuvers from 13 intersections were extracted from the Second Strategic Highway Research Program (SHRP2) database and categorized based on whether or not the vehicle came to a stop prior to making its turn. Out of the vehicles that did stop, those that were the first and second in line at the intersection were isolated. This resulted in 186 stopped first-in-line turns, 91 stopped second-in-line turns, and 353 no stop turns. Independent variables regarding the maneuver, including driver’s sex and age, vehicle type, speed, and longitudinal and lateral acceleration were extracted. The on-board video was reviewed to categorize the road as dry/wet and if it was day/night. Aerial photographs of the intersections were obtained, and the inner radius of the curve was measured using the curb as a reference. For vehicles that stopped at the intersection, we generated a parametrized lateral and longitudinal acceleration profile. For the vehicles that did not stop prior to making their right turn, parameterized models of the speed and lateral acceleration were calculated. Linear regression models were created to understand the effect of the independent variables listed above for both the stopped and not-stopped data. This research provides valuable insight into the typical accelerations and speeds of vehicles during right-turn maneuvers, and the results are directly applicable to collision reconstructions.
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DOI
https://doi.org/10.4271/2024-01-2472
Pages
12
Citation
Flynn, T., Wilkinson, C., and Siegmund, G., "Typical Acceleration and Speed Profiles for Right-Turn Maneuvers Based on SHRP2 Naturalistic Driving Data," SAE Technical Paper 2024-01-2472, 2024, https://doi.org/10.4271/2024-01-2472.
Additional Details
Publisher
Published
Apr 09
Product Code
2024-01-2472
Content Type
Technical Paper
Language
English