Novel Planning-based Algorithms for Human Motion Prediction

International audience— Human motion prediction from visual tracking is a challenging problem with a wide array of applications such as robotics, video surveillance and situation understanding. Recently, planning-based methods –which assume that people move by planning over a cost function– have emerged as one of the most promising alternatives. Nevertheless, state of the art planning based algorithms have shortcomings regarding their computational complexity and ability to predict for arbitrary time intervals. This paper addresses these shortcomings by leveraging alternative planning techniques (Fast Marching Method) and formulating efficient algorithms for goal estimation and full spatiotemporal prediction with lower complexity than comparable approaches. In preliminary experiments, the proposed method significantly outperforms the accuracy of the current state-of-the-art approach while reducing the computation time by a factor of 30 using a parallel version of our algorithm

A survey on motion prediction and risk assessment for intelligent vehicles

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

Estimation de mouvement des obstacles mobiles: une approche statistique

voir basilic : http://emotion.inrialpes.fr/bibemotion/2003/Vas03/ type: Mémoire de Diplôme d'Etudes Approfondies institution: Inst. Nat. Polytechnique de Grenoble address: Grenoble (FR) The main objective of this work is to search for a motion estimation technique for vehicles and pedestrians having the following properties: It should produce estimations with a long Time Horizon. It should be as general as possible and work with many diJerent kinds of objects. It should be fast enough to give estimations in real time. We propose a motion estimation technique based on pairwise clustering which verifies the required properties. We have implemented and tested our approach, comparing it with a technique that we consider to represent the state of the art in clustering techniques. In order to perform the comparison, we propose a benchmark that can be used to test other motion estimation techniques. </p

Prema sigurnoj navigaciji vozila u dinamičkim urbanim scenarijima

This 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).Ovaj članak 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 gibanja čiji 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 izostavljenou analizama)

Geometric and Bayesian models for safe navigation in dynamic environments

Autonomous navigation in open and dynamic environments is an important challenge, requiring to solve several difficult research problems located on the cutting edge of the state of the art. Basically, these problems may be classified into three main categories: (a) SLAM in dynamic environments; (b) detection, characterization, and behavior prediction of the potential moving obstacles; and (c) online motion planning and safe navigation decision based on world state predictions. This paper addresses some aspects of these problems and presents our latest approaches and results. The solutions we have implemented are mainly based on the followings paradigms: multiscale world representation of static obstacles based on the wavelet occupancy grid; adaptative clustering for moving obstacle detection inspired on Kohonen networks and the growing neural gas algorithm; and characterization and motion prediction of the observed moving entities using Hidden Markov Models coupled with a novel algorithm for structure and parameter learnin

Growing Hidden Markov Models: An Incremental Tool for Learning and Predicting Human and Vehicle Motion

International audienceModeling and predicting human and vehicle motion is an active research domain. Due to the difficulty of modeling the various factors that determine motion (e.g. internal state, perception) this is often tackled by applying machine learning techniques to build a statistical model, using as input a collection of trajectories gathered through a sensor (e.g. camera, laser scanner), and then using that model to predict further motion. Unfortunately, most current techniques use off-line learning algorithms, meaning that they are not able to learn new motion patterns once the learning stage has finished. In this paper, we present an approach where motion patterns can be learned incrementally, and in parallel with prediction. Our work is based on a novel extension to Hidden Markov Models --called Growing Hidden Markov models -- which gives us the ability to learn incrementally both the parameters and the structure of the model. The proposed approach has been evaluated using synthetic and real trajectory data. In our experiments our approach consistently learned motion models that were more compact and accurate than those produced by two other state of the art techniques

Incremental Learning of Statistical Motion Patterns with Growing Hidden Markov Models

International audienceModeling and predicting human and vehicle motion is an active research domain. Due to the difficulty of modeling the various factors that determine motion (e.g. internal state, perception, etc.) this is often tackled by applying machine learning techniques to build a statistical model, using as input a collection of trajectories gathered through a sensor (e.g. camera, laser scanner), and then using that model to predict further motion. Unfortunately, most current techniques use off-line learning algorithms, meaning that they are not able to learn new motion patterns once the learning stage has finished. In this paper, we present an approach where motion patterns can be learned incrementally, and in parallel with prediction. Our work is based on a novel extension to Hidden Markov Models - called Growing Hidden Markov models - which gives us the ability to learn incrementally both the parameters and the structure of the model

Safe Vehicle Navigation in Dynamic Urban Scenarios

International audienceThis 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)

Intentional Motion On-line Learning and Prediction

International audiencePredicting motion of humans, animals and other objects which move according to internal plans is a challenging problem. Most existing approaches operate in two stages: a) learning typical motion patterns by observing an environment and b) predicting future motion on the basis of the learned patterns. In existing techniques, learning is performed off-line, hence, it is impossible to refine the existing knowledge on the basis of the new observations obtained during the prediction phase. We propose an approach which uses Hidden Markov Models to represent motion patterns. It is different from similar approaches because it is able to learn and predict in a concurrent fashion thanks to a novel approximate learning approach, based on the Growing Neural Gas algorithm, which estimates both HMM parameters and structure. The found structure has the property of being a planar graph, thus enabling exact inference in linear time with respect to the number of states in the model. Our experiments demonstrate that the technique works in real-time, and is able to produce sound long-term predictions of people motion

Safe Vehicle Navigation in Dynamic Urban Environments: A Hierarchical Approach

International audienceThis 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 Plan- ner 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)