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This paper describes a novel method for allowing an autonomous ground vehicle to predict the intent of other agents in an urban environment.
Monte carlo filter and smoother for non-gaussian nonlinear state space models
G. Kitagawa · 1996
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Intelligent vehicle applications worldwide
R. Bishop · 2000
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Socio-economic impact assessment of intelligent transport systems
Z. Juan, J. Wu, and M. McDonald · 2006
Earlier work this paper cites.
Recursive agent modeling with probabilistic velocity obstacles for mobile robot navigation among humans
B. Kluge and E. Prassler · 2007
Earlier work this paper cites.
Understanding human intentions via hidden markov models in autonomous mobile robots
R. Kelley, A. Tavakkoli, C. King, M. Nicolescu, M. Nicolescu, and G. Bebis · 2008
Earlier work this paper cites.
Modelling pedestrian trajectory patterns with gaussian processes
D. Ellis, E. Sommerlade, and I. Reid · 2009
Cited alongside, same era.
Incremental learning of statistical motion patterns with growing hidden markov models
D. Vasquez, T. Fraichard, and C. Laugier · 2009
Cited alongside, same era.
Bayesian theory of mind: Modeling joint belief-desire attribution
C. L. Baker, R. R. Saxe, and J. B. Tenenbaum · 2011
Cited alongside, same era.
Social ramifications of autonomous urban land vehicles
T. C. Folsom · 2011
Cited alongside, same era.
Reciprocal collision avoidance for multiple car-like robots
J. Alonso-Mora, A. Breitenmoser, P. Beardsley, and R. Siegwart · 2012
Cited alongside, same era.
Intention-aware pedestrian avoidance
T. Bandyopadhyay, C. Z. Jie, D. Hsu, M. H. Ang Jr, D. Rus, and E. Frazzoli · 2013
Later among the works it cites.
Discrete and continuous, probabilistic anticipation for autonomous robots in urban environments
F. Havlak and M. Campbell · 2014
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http://www.driveandstayalive.com/Info%20Section/stopping-distances.htm
Drive and stay alive, inc · 2015
Closest in time.
http://www.who.int/gho/road_safety/mortality/traffic_deaths_number/en/
Global health observatory (gho) data · 2015
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