Intention-aware risk estimation: Field results
| Autor: | Stephanie Lefevre, Dizan Vasquez, Javier Ibanez-Guzman, Christian Laugier |
|---|---|
| Přispěvatelé: | Department of Electrical Engineering and Computer Science [Berkeley] (EECS), University of California [Berkeley], University of California-University of California, Robots coopératifs et adaptés à la présence humaine en environnements dynamiques (CHROMA), Inria Grenoble - Rhône-Alpes, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-CITI Centre of Innovation in Telecommunications and Integration of services (CITI), Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National des Sciences Appliquées (INSA), Multimedia and Driving Assistance Systems, RENAULT, University of California [Berkeley] (UC Berkeley), University of California (UC)-University of California (UC) |
| Jazyk: | angličtina |
| Rok vydání: | 2015 |
| Předmět: |
Interpretation (logic)
Computer science business.industry Perspective (graphical) Probabilistic logic Computer security computer.software_genre Machine learning Field (computer science) Order (business) Trajectory Wireless [INFO.INFO-RB]Computer Science [cs]/Robotics [cs.RO] Artificial intelligence business Hidden Markov model computer ComputingMilieux_MISCELLANEOUS |
| Zdroj: | IEEE International Workshop on Advanced Robotics and its Social Impacts IEEE International Workshop on Advanced Robotics and its Social Impacts, Jul 2015, Lyon, France ARSO |
| Popis: | This paper tackles the risk estimation problem from a new perspective: a framework is proposed for reasoning about traffic situations and collision risk at a semantic level, while classic approaches typically reason at a trajectory level. Risk is assessed by estimating the intentions of drivers and detecting conflicts between them, rather than by predicting the future trajectories of the vehicles and detecting collisions between them. More specifically, dangerous situations are identified by comparing what drivers intend to do with what they are expected to do according to the traffic rules. The reasoning is performed in a probabilistic manner, in order to take into account sensor uncertainties and interpretation ambiguities. This framework can in theory be applied to any type of traffic situation; here we present its application to road intersections. The approach was validated with field trials using passenger vehicles equipped with Vehicle-to-Vehicle wireless communication modems. The results demonstrate that the algorithm is able to detect dangerous situations early and complies with real-time constraints. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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