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Citation |
Wang S, Li Z. Transp. Res. Rec. 2019; 2673(5): 49-60. |
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Copyright |
(Copyright © 2019, Transportation Research Board, National Research Council, National Academy of Sciences USA, Publisher SAGE Publishing) |
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DOI |
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PMID |
unavailable |
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Abstract |
Horizontal curves are a major cause of road departure crashes that lead to fatal and severe injuries. Existing curve crash avoidance systems are typically enabled by displaying safety messages via in-vehicle heads-up/down display. However, these systems just pass the information to drivers. The final decision to reduce or maintain speed in response to the safety message is still the human driver's. Because of the involvement of the human factor, there is potential for road departure crashes to happen if human drivers do not respond to safety messages appropriately. Autonomous vehicle (AV) technology targets elimination of human error in driving through an automated driving system. In this context, this paper proposes a conceptual prototype of a connected and AV-based horizontal curve crash avoidance system (CAV-HCCAS), aiming at achieving a permanent solution to horizontal curve safety by excluding human error through automated driving. In CAV-HCCAS, a roadside sensor detects pavement wetness level at the horizontal curve and communicates it to the vehicle via dedicated short-range communication. By processing the pavement wetness information, the AV applies a safe curve travel speed that reflects the real-time pavement conditions. An automated driving simulation experiment was performed to prove the concept. Dry and wet pavement conditions were simulated for a horizontal curve. Lane deviation data and the resulting lane departure conflicts were measured as safety performance measures. Language: en |