Enhancing Mobile Object Classification Using Geo-referenced Maps and Evidential Grids

Evidential grids have recently shown interesting properties for mobile object perception. Evidential grids are a generalisation of Bayesian occupancy grids using Dempster- Shafer theory. In particular, these grids can handle efficiently partial information. The novelty of this article is to propose a perception scheme enhanced by geo-referenced maps used as an additional source of information, which is fused with a sensor grid. The paper presents the key stages of such a data fusion process. An adaptation of conjunctive combination rule is presented to refine the analysis of the conflicting information. The method uses temporal accumulation to make the distinction between stationary and mobile objects, and applies contextual discounting for modelling information obsolescence. As a result, the method is able to better characterise the occupied cells by differentiating, for instance, moving objects, parked cars, urban infrastructure and buildings. Experiments carried out on real- world data illustrate the benefits of such an approach.Comment: 6 pp. arXiv admin note: substantial text overlap with arXiv:1207.101

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