Statistical accident analysis supporting the control of autonomous vehicles

[1]  S. S. S. Yahaya,et al.  Sensitivity analysis of Welch's t-test , 2014 .

[2]  Tessa K Anderson,et al.  Kernel density estimation and K-means clustering to profile road accident hotspots. , 2009, Accident; analysis and prevention.

[3]  Maen Ghadi,et al.  A comparative analysis of black spot identification methods and road accident segmentation methods. , 2019, Accident; analysis and prevention.

[4]  Richard Andrášik,et al.  Identification of hazardous road locations of traffic accidents by means of kernel density estimation and cluster significance evaluation. , 2013, Accident; analysis and prevention.

[5]  Constantine Samaras,et al.  Cost and benefit estimates of partially-automated vehicle collision avoidance technologies. , 2016, Accident; analysis and prevention.

[6]  Bhagwant Persaud,et al.  Safety Prediction Models , 2007 .

[7]  Kip Smith,et al.  Pedestrian injury mitigation by autonomous braking. , 2010, Accident; analysis and prevention.

[8]  Mohamed Abdel-Aty,et al.  Real-time crash prediction models: State-of-the-art, design pathways and ubiquitous requirements. , 2019, Accident; analysis and prevention.

[9]  Yuanchang Xie,et al.  Crash frequency analysis of different types of urban roadway segments using generalized additive model. , 2012, Journal of safety research.

[10]  Pál Hegyi,et al.  Searching possible accident black spot locations with accident analysis and GIS software based on GPS coordinates , 2017 .

[11]  Francisco Alonso,et al.  Knowledge of the Concepts of "Black Spot", "Grey Spot" and "High Accident Concentration Sections" Among Drivers , 2016 .

[12]  Anders Kullgren,et al.  A test-based method for the assessment of pre-crash warning and braking systems. , 2013, Accident; analysis and prevention.

[13]  Sandor Szenasi PLANAR SLIDING WINDOW TECHNIQUE FOR SEARCHING ACCIDENT HOT SPOTS , 2011 .

[14]  S. Lassarre,et al.  A new theory of complexity for safety research. The case of the long-lasting gap in road safety outcomes between France and Great Britain , 2014 .

[15]  Ezra Hauer,et al.  Estimating Safety by the Empirical Bayes Method: A Tutorial , 2002 .

[16]  Sandor Szenasi,et al.  A method to identify black spot candidates in built-up areas , 2017 .

[17]  Michel Mouchart,et al.  The local spatial autocorrelation and the kernel method for identifying black zones. A comparative approach. , 2003, Accident; analysis and prevention.

[18]  Wen Cheng,et al.  Experimental evaluation of hotspot identification methods. , 2005, Accident; analysis and prevention.

[19]  Nick Hounsell,et al.  Driver response to variable message sign information in London , 2002 .

[20]  Geert Wets,et al.  Ranking and selecting dangerous crash locations: correcting for the number of passengers and Bayesian ranking plots. , 2006, Journal of safety research.

[21]  Jessica S Jermakian,et al.  Crash avoidance potential of four passenger vehicle technologies. , 2011, Accident; analysis and prevention.

[22]  Will Murray,et al.  Work-Related Road Safety: A Case Study of Roche Australia , 2012 .

[23]  Sandor M. Veres,et al.  Autonomous vehicle control systems — a review of decision making , 2011 .

[24]  Daniel Stojcsics Autonomous Waypoint-based Guidance Methods for Small Size Unmanned Aerial Vehicles , 2014 .

[25]  Hao Wang,et al.  Comparative analysis of the spatial analysis methods for hotspot identification. , 2014, Accident; analysis and prevention.

[27]  Mario Romero,et al.  Crash Databases in Australasia, the European Union, and the United States , 2013 .

[28]  Philippe Nitsche,et al.  Pre-crash scenarios at road junctions: A clustering method for car crash data. , 2017, Accident; analysis and prevention.

[29]  James Lenard,et al.  Typical pedestrian accident scenarios for the development of autonomous emergency braking test protocols. , 2014, Accident; analysis and prevention.