International audienceWith the objective to improve road safety, the automotive industry is moving toward more “intelligent” vehicles. One of the major challenges is to detect dangerous situations and react accordingly in order to avoid or mitigate accidents. This requires predicting the likely evolution of the current traffic situation, and assessing how dangerous that future situation might be. This paper is a survey of existing methods for motion prediction and risk assessment for intelligent vehicles. The proposed classification is based on the semantics used to define motion and risk. We point out the tradeoff between model completeness and real-time constraints, and the fact that the choice of a risk assessment method is influenced by the selected motion model
International audience— For mobile robots which operate in human pop-ulated environments, modeling social interactions is key to understand and reproduce people's behavior. A promising approach to this end is Inverse Reinforcement Learning (IRL) as it allows to model the factors that motivate people's actions instead of the actions themselves. A crucial design choice in IRL is the selection of features that encode the agent's context. In related work, features are typically chosen ad hoc without systematic evaluation of the alternatives and their actual impact on the robot's task. In this paper, we introduce a new software framework to systematically investigate the effect features and learning algorithms used in the literature. We also present results for the task of socially compliant robot navigation in crowds, evaluating two different IRL approaches and several feature sets in large-scale simulations. The results are benchmarked according to a proposed set of objective and subjective performance metrics
Original scientific paperThis paper describes the deliberative part of a navigation architecture designed for safe vehicle navigation in dynamic urban environments. It comprises two key modules working together in a hierarchical fashion: (a) the Route Planner whose purpose is to compute a valid itinerary towards the a given goal. An itinerary comprises a geometric path augmented with additional information based on the structure of the environment considered and traffic regulations, and (b) the Partial Motion Planner whose purpose is to ensure the proper following of the itinerary while dealing with the moving objects present in the environment (eg other vehicles, pedestrians).In the architecture proposed, a special attention is paid to the motion safety issue, ie the ability to avoid collisions. Different safety levels are explored and their operational conditions are explicitly spelled out (something which is usually not done). Key words: Motion planning, Dynamic environment, Motion safety, Urban navigationPrema sigurnoj navigaciji vozila u dinamičkim urbanim scenarijima. Ovajčlanak opisuje ciljno orijentirani dio navigacijske arhitekture za sigurnu navigaciju vozilima u dinamičkim urbanim sredinama. Sastoji se od dva važna modula, koji su hierarhijski povezani: (a) Planer puta koji je odgovoran za pronalaženje valjane globalne rute prema zadanom cilju -ta ruta se sastoji od geometrijskog puta sa dodatnim informacijama u odnosu na zadanu strukturu okoline i regulaciju prometa; (b) Parcijalni planer gibanjačiji zadatak je slijeđenje zadane globalne rute uz navigaciju u prisutnosti pokretnih objekata u okolini (npr. ostala vozila i pješaci).U predloženoj arhitekturi posebna pažnja se pridodaje sigurnosti gibanja, dakle sposobnosti izbjegavanja sudara. Razmotrene su različite razine sigurnosti uz izričiti opis njihovih zadanih režima rada (što je uobičajeno izostavljeno u analizama).
This paper proposes a technique to obtain long term estimates of the motion of a moving object in a structured environment. Ob jects moving in such environments often participate in typical motion patterns which can be observed consistently. Our technique learns those patterns by observing the environment and clustering the observed trajectories using any pairwise clustering algorithm. We have implemented our technique using both simulated and real data coming from a vision system. The results show that the technique is general, produces long-term predictions and is fast enough for its use in real time applications.
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