Citations (35)
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John M. Scanlon, Rini Sherony & Hampton C. Gabler. (2016) Predicting crash-relevant violations at stop sign–controlled intersections for the development of an intersection driver assistance system. Traffic Injury Prevention 17:sup1, pages 59-65.
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John M. Scanlon, Kristofer D. Kusano, Tom Daniel, Christopher Alderson, Alexander Ogle & Trent Victor. (2021) Waymo simulated driving behavior in reconstructed fatal crashes within an autonomous vehicle operating domain. Accident Analysis & Prevention 163, pages 106454.
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Justin A. Mahlberg, Rahul Suryakant Sakhare, Howell Li, Jijo K. Mathew, Darcy M. Bullock & Gopi C. Surnilla. (2021) Prioritizing Roadway Pavement Marking Maintenance Using Lane Keep Assist Sensor Data. Sensors 21:18, pages 6014.
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Hong Tan, Fuquan Zhao, Han Hao & Zongwei Liu. (2021) Evidence for the Crash Avoidance Effectiveness of Intelligent and Connected Vehicle Technologies. International Journal of Environmental Research and Public Health 18:17, pages 9228.
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Apoorva P. Hungund, Ganesh Pai & Anuj K. Pradhan. (2021) Systematic Review of Research on Driver Distraction in the Context of Advanced Driver Assistance Systems. Transportation Research Record: Journal of the Transportation Research Board 2675:9, pages 756-765.
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Bjorn Olofsson & Lars Nielsen. (2021) Using Crash Databases to Predict Effectiveness of New Autonomous Vehicle Maneuvers for Lane-Departure Injury Reduction. IEEE Transactions on Intelligent Transportation Systems 22:6, pages 3479-3490.
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Ane Dalsnes Storsæter, Kelly Pitera & Edward McCormack. (2021) Using ADAS to Future-Proof Roads—Comparison of Fog Line Detection from an In-Vehicle Camera and Mobile Retroreflectometer. Sensors 21:5, pages 1737.
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Alexandros Leledakis, Magdalena Lindman, Jonas Östh, Linus Wågström, Johan Davidsson & Lotta Jakobsson. (2021) A method for predicting crash configurations using counterfactual simulations and real-world data. Accident Analysis & Prevention 150, pages 105932.
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Maria Hagl & Dongo Rémi Kouabenan. (2020) Safe on the road – Does Advanced Driver-Assistance Systems Use affect Road Risk Perception?. Transportation Research Part F: Traffic Psychology and Behaviour 73, pages 488-498.
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Ling Wang, Hao Zhong, Wanjing Ma, Mohamed Abdel-Aty & Juneyoung Park. (2020) How many crashes can connected vehicle and automated vehicle technologies prevent: A meta-analysis. Accident Analysis & Prevention 136, pages 105299.
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Xing Ma, Chunyang Mu, Xiaolong Wang & Jianyu Chen. (2019) Projective Geometry Model for Lane Departure Warning System in Webots. Projective Geometry Model for Lane Departure Warning System in Webots.
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John Michael Scanlon, Rini Sherony & Hampton Clay Gabler. (2017) Earliest Sensor Detection Opportunity for Left Turn Across Path Opposite Direction Crashes. IEEE Transactions on Intelligent Vehicles 2:1, pages 62-70.
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Rui Sun, Yucheng Zhang, Hongyang Bai & Wenjie Su. (2016) Fuzzy logic based approach and sensitivity analysis of irregular driving detection algorithm. Fuzzy logic based approach and sensitivity analysis of irregular driving detection algorithm.
John M. Scanlon, Rini Sherony & Hampton C. Gabler. (2016) Preliminary potential crash prevention estimates for an Intersection Advanced Driver Assistance System in straight crossing path crashes. Preliminary potential crash prevention estimates for an Intersection Advanced Driver Assistance System in straight crossing path crashes.
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John M. Scanlon, Kristofer D. Kusano & Hampton C. Gabler. (2018) Lane Departure Warning and Prevention Systems in the U.S. Vehicle Fleet: Influence of Roadway Characteristics on Potential Safety Benefits. Transportation Research Record: Journal of the Transportation Research Board 2559:1, pages 17-23.
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