In-depth analysis of crash contributing factors and potential ADAS interventions among at-risk drivers using the SHRP 2 naturalistic driving study

Abstract

Objective

Motor vehicle crashes remain a significant problem. Advanced driver assistance systems (ADAS) have the potential to reduce crash incidence and severity, but their optimization requires a comprehensive understanding of driver-specific errors and environmental hazards in real-world crash scenarios. Therefore, the objectives of this study were to quantify contributing factors using the Strategic Highway Research Program 2 (SHRP 2) Naturalistic Driving Study (NDS), identify potential ADAS interventions, and make suggestions to optimize ADAS for real-world crash scenarios.

Methods

A subset of the SHRP 2 NDS consisting of at-fault crashes (n = 369) among teens (16–19 yrs), young adults (20–24 yrs), adults (35–54 yrs) and older adults (70+ yrs) were reviewed to identify contributing factors and potential ADAS interventions. Contributing factors were classified according to National Motor Vehicle Crash Causation Survey pre-crash assessment variable elements. A single critical factor was selected among the contributing factors for each crash. Case reviews with a multidisciplinary panel of industry experts were conducted to develop suggestions for ADAS optimization. Critical factors were compared across at-risk driving groups, gender, and incident type using chi-square statistics and multinomial logistic regression.

Results

Driver error was the critical factor in 94% of crashes. Recognition error (56%), including internal distraction and inadequate surveillance, was the most common driver error sub-type. Teens and young adults exhibited greater decision errors compared to older adults (p < 0.01). Older adults exhibited greater performance errors (p < 0.05) compared to teens and young adults. Automatic emergency braking (AEB) had the greatest potential to mitigate crashes (48%), followed by vehicle-to-vehicle communication (38%) and driver monitoring (24%). ADAS suggestions for optimization included (1) implementing adaptive forward collision warning, AEB, high-speed warning, and curve-speed warning to account for road surface conditions (2) ensuring detection of nonstandard road objects, (3) vehicle-to-vehicle communication alerting drivers to cross-traffic, (4) vehicle-to-infrastructure communication alerting drivers to the presence of pedestrians in crosswalks, and (5) optimizing lane keeping assist for end-departures and pedal confusion.

Conclusions

These data provide stakeholders with a comprehensive understanding of critical factors among at-risk drivers as well as suggestions for ADAS improvements based on naturalistic data. Such data can be used to optimize ADAS for driver-specific errors and help develop more robust vehicle test procedures.

Keywords:

• Teen driving
• ADAS
• naturalistic
• at-risk drivers
• crash
• SHRP 2

Acknowledgments

The authors would like to thank the industry experts that participated in the case reviews for their time and valued insight. The authors would like to acknowledge Kevin Heller, Program Coordinator at the Children’s Hospital of Philadelphia, for his assistance conducting the case review webinars. The authors would like to acknowledge Walter Faig, Senior Biostatistician at the Children’s Hospital of Philadelphia, for his assistance with the statistical analysis.

Additional information

Funding

The authors acknowledge the National Science Foundation (NSF) Center for Child Injury Prevention Studies (CChIPS) IU/CRC at the Children’s Hospital of Philadelphia (CHOP) and the Ohio State University (OSU) for sponsoring this study and its Industry Advisory Board (IAB) members for their support, valuable input, and advice. This material is also based upon work supported by the National Science Foundation under Grant Number EEC-1460927. The views presented here are solely those of the authors and not necessarily the views of CHOP, OSU, the NSF, or the IAB members.