Assessment of the safety benefits of vehicles' advanced driver assistance, connectivity and low level automation systems. (August 2018)

• Record Type: Journal Article
• Authors: Yue, Lishengsa; Abdel-Aty, Mohamed; Wu, Yina; Wang, Ling
• Is Part Of: Accident analysis and prevention. Volume 117(2018)
• Page Start: 55
• Page End: 64
• Publication Date: 2018-08
• Subjects: Crash avoidance effectiveness -- Connected vehicle technology -- Driving assistance technology -- Safety estimation methodology -- Pre-Crash scenarios; Accidents -- Prevention -- Periodicals; Accident Prevention -- Periodicals; Accidents -- Prévention -- Périodiques; 363.106
• DOI: 10.1016/j.aap.2018.04.002
• Languages: English
• ISSNs: 0001-4575

Abstract

Highlights: The CV&DA technologies tend to have a better safety benefit on heavy trucks when compared with light vehicles. The CV&DA technologies hardly have an effectiveness over 70% in the actual environment. Forward Collision Warning could reduce 35% of the near-crash events under fog conditions. The maximum reduction of total crash events is about 50%. The rear-end crashes for both light vehicles and heavy trucks have the most expected crash benefits from the CV&DA technologies. Abstract: The Connected Vehicle (CV) technologies together with other Driving Assistance (DA) technologies are believed to have great effects on traffic operation and safety, and they are expected to impact the future of our cities. However, few research has estimated the exact safety benefits when all vehicles are equipped with these technologies. This paper seeks to fill the gap by using a general crash avoidance effectiveness framework for major CV&DA technologies to make a comprehensive crash reduction estimation. Twenty technologies that were tested in recent studies are summarized and sensitivity analysis is used for estimating their total crash avoidance effectiveness. The results show that crash avoidance effectiveness of CV&DA technology is significantly affected by the vehicle type and the safety estimation methodology. A 70% crash avoidance rate seems to be the highest effectiveness for the CV&DA technologies operating in the real-world environment. Based on the 2005–2008 U.S. GES Crash Records, this research found that the CV&DA technologies could lead to the reduction of light vehicles' crashes and heavy trucks' crashes by at least 32.99% and 40.88%, respectively. The rear-end crashes for both light vehicles and heavy trucks have the most expected crash benefits from the technologies. The paper also studies the effectiveness of Forward Collision Warning technology (FCW) under fog conditions, and the results show that FCW could reduce 35% of the near-crash events under fog conditions.