6,457 research outputs found
Dynamic Occupancy Grid Prediction for Urban Autonomous Driving: A Deep Learning Approach with Fully Automatic Labeling
Long-term situation prediction plays a crucial role in the development of
intelligent vehicles. A major challenge still to overcome is the prediction of
complex downtown scenarios with multiple road users, e.g., pedestrians, bikes,
and motor vehicles, interacting with each other. This contribution tackles this
challenge by combining a Bayesian filtering technique for environment
representation, and machine learning as long-term predictor. More specifically,
a dynamic occupancy grid map is utilized as input to a deep convolutional
neural network. This yields the advantage of using spatially distributed
velocity estimates from a single time step for prediction, rather than a raw
data sequence, alleviating common problems dealing with input time series of
multiple sensors. Furthermore, convolutional neural networks have the inherent
characteristic of using context information, enabling the implicit modeling of
road user interaction. Pixel-wise balancing is applied in the loss function
counteracting the extreme imbalance between static and dynamic cells. One of
the major advantages is the unsupervised learning character due to fully
automatic label generation. The presented algorithm is trained and evaluated on
multiple hours of recorded sensor data and compared to Monte-Carlo simulation
Human Motion Trajectory Prediction: A Survey
With growing numbers of intelligent autonomous systems in human environments,
the ability of such systems to perceive, understand and anticipate human
behavior becomes increasingly important. Specifically, predicting future
positions of dynamic agents and planning considering such predictions are key
tasks for self-driving vehicles, service robots and advanced surveillance
systems. This paper provides a survey of human motion trajectory prediction. We
review, analyze and structure a large selection of work from different
communities and propose a taxonomy that categorizes existing methods based on
the motion modeling approach and level of contextual information used. We
provide an overview of the existing datasets and performance metrics. We
discuss limitations of the state of the art and outline directions for further
research.Comment: Submitted to the International Journal of Robotics Research (IJRR),
37 page
Advances in the Bayesian Occupancy Filter framework using robust motion detection technique for dynamic environment monitoring
International audienceThe Bayesian Occupancy Filter provides a framework for grid-based monitoring of the dynamic environment. It allows to estimate dynamic grids, containing both information of occupancy and velocity. Clustering such grids then provides detection of the objects in the observed scene. In this paper we present recent improvements in this framework. First, multiple layers from a laser scanner are fused using opinion pool, to deal with conflicting information. Then a fast motion detection technique based on laser data and odometer/IMU information is used to separate the dynamic environment from the static one. This technique instead of performing a complete SLAM (Simultaneous Localization and Mapping) solution, is based on transferring occupancy information between consecutive data grids, the objective is to avoid false positives (static objects) like other DATMO approaches. Finally, we show the integration with Bayesian Occupancy Filter (BOF) and with the subsequent tracking module called Fast Clustering-Tracking Algorithm (FCTA). We especially show the improvements achieved in tracking results after this integration, for an intelligent vehicle application
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