Probabilistic risk assessment for pedestrian–vehicle collision considering uncertainties of pedestrian mobility

References

• Althoff M, Stursberg O, Buss M. Stochastic reachable sets of interacting traffic participants. Paper presented at: IEEE Intelligent Vehicles Symposium Eindhoven University of Technology; June 4–6, 2008; Eindhoven, The Netherlands.
   Google Scholar
• Berthelot A, Tamke A, Dang T, Breuel G. Handling uncertainties in criticality assessment. Paper presented at: IEEE Intelligent Vehicles Symposium (IV); June 5–9, 2011; Baden-Baden, Germany.
   Google Scholar
• Berthelot A, Tamke A, Dang T, Breuel G. A novel approach for the probabilistic computation of time-to-collision. Paper presented at: Intelligent Vehicles Symposium; June 3–7, 2012; Alcalá de Henares, Spain.
   Google Scholar
• Braeuchle C, Flehmig F, Rosenstiel W, Kropf T. Maneuver decision for active pedestrian protection under uncertainty. Paper presented at: 16th International IEEE Annual Conference on Intelligent Transportation Systems; October 6–9, 2013; The Hague, The Netherlands.
   Google Scholar
• Eidehall A, Petersson L. Statistical threat assessment for general road scenes using Monte Carlo sampling. IEEE Trans Intell Transp Syst. 2008;9:137–147.
   Web of Science ®Google Scholar
• Ellis D, Sommerlade E, Reid I. Modelling pedestrian trajectory patterns with Gaussian processes. Paper presented at: IEEE 12th International Conference on Computer Vision Workshops; September 27–October 4, 2009; Kyoto, Japan.
   Google Scholar
• Gandhi T, Trivedi M. Pedestrian protection systems: issues, survey, and challenges. IEEE Trans Intell Transp Syst. 2007;8:413–430.
   Web of Science ®Google Scholar
• Goldhammer M, Gerhard M, Zernetsch S, Doll K, Brunsman U. Early prediction of a pedestrian's trajectory at intersections. Paper presented at: 16th International IEEE Annual Conference on Intelligent Transportation Systems; October 6–9, 2013; The Hague, The Netherlands.
   Google Scholar
• Hillenbrand J, Spieker A, Kroschel K. Efficient decision making for a multi-level collision mitigation system. Paper presented at: Intelligent Vehicles Symposium; June 13–15, 2006; Tokyo, Japan.
   Google Scholar
• Julier S, Uhlmann J. A General Method for Approximating Nonlinear Transformations of Probability Distributions. Oxford, UK: Robotics Research Group, Department of Engineering Science, University of Oxford; 1996.
   Google Scholar
• Julier S, Uhlmann J. Unscented filtering and nonlinear estimation. Proc IEEE. 2004;92:401–422.
   Web of Science ®Google Scholar
• Keller C, Gavrila D. Will the pedestrian cross? A study on pedestrian path prediction. IEEE Trans Intell Transp Syst. 2014;15:494–506.
   Web of Science ®Google Scholar
• Köhler S, Goldhammer M, Bauer S, Doll K, Brunsmann U, Dietmayer K. Early detection of the pedestrian's intention to cross the street. Paper presented at: 15th International IEEE Conference on Intelligent Transportation Systems; September 16–19, 2012; Anchorage, AK.
   Google Scholar
• Lambert A, Gruyer D, Pierre G, Ndjeng A. Collision probability assessment for speed control. Paper presented at: 11th International IEEE Conference on Intelligent Transportation Systems; October 12–15, 2008; Beijing, China.
   Google Scholar
• Nakatsubo K, Yamada K. Detecting unusual pedestrian behavior toward own vehicle for vehicle-to-pedestrian collision avoidance. Paper presented at: IEEE Intelligent Vehicles Symposium; June 21–24, 2010; San Diego, CA.
   Google Scholar
• Nicolao G, Ferrara A, Giacomini L. Onboard sensor-based collision risk assessment to improve pedestrians’ safety. IEEE Transactions on Vehicular Technology. 2007;56:2405–2413.
   Web of Science ®Google Scholar
• Patil S, Berg J, Alterovitz R. Estimating probability of collision for safe motion planning under Gaussian motion and sensing uncertainty. Paper presented at: IEEE International Conference on Robotics and Automation; May 14–18, 2012; Saint Paul, MN.
   Google Scholar
• Rasmussen C, Williams C. Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning). Cambridge, MA: The MIT Press; 2006.
   Google Scholar
• Schubert R, Richter E, Wanielik G. Comparison and evaluation of advanced motion models for vehicle tracking. Paper presented at: 11th International Conference on Information Fusion; June 30–July 3, 2008; Cologne, Germany.
   Google Scholar
• Steinhoff U, Schiele B. Dead reckoning from the pocket—an experimental study. Paper presented at: IEEE International Conference on Pervasive Computing and Communications; March 29–April 2, 2010; Mannheim, Germany.
   Google Scholar
• Tamke A, Dang T, Breuel G. A flexible method for criticality assessment in driver assistance systems. Paper presented at: IEEE Intelligent Vehicles Symposium (IV); June 5–9, 2011; Baden-Baden, Germany.
   Google Scholar
• World Health Organization. Global Status Report on Road Safety 2013: Supporting a Decade of Action. Geneva, Switzerland: Author; 2013.
   Google Scholar
• Zhang Y, Yao D, Qiu T, Peng L. Scene-based pedestrian safety performance model in mixed traffic situation. IET Intelligent Transport Systems. 2014;8(3):209–218.
   Web of Science ®Google Scholar
• Zhang Y, Yao D, Qiu T, Peng L, Zhang Y. Pedestrian safety analysis in mixed traffic conditions using video data. IEEE Trans Intell Transp Syst. 2012;13:1832–1844.
   Web of Science ®Google Scholar
• Zhuang X, Wu C. Modeling pedestrian crossing paths at unmarked roadways. IEEE Trans Intell Transp Syst. 2013;14:1438–1448.
   Web of Science ®Google Scholar