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.
In this work, we tackle the problem of modeling the vehicle environment as dynamic occupancy grid map in complex urban scenarios using recurrent neural networks. Dynamic occupancy grid maps represent the scene in a bird's eye view, where each grid cell contains the occupancy probability and the two dimensional velocity. As input data, our approach relies on measurement grid maps, which contain occupancy probabilities, generated with lidar measurements. Given this configuration, we propose a recurrent neural network architecture to predict a dynamic occupancy grid map, i.e. filtered occupancy and velocity of each cell, by using a sequence of measurement grid maps. Our network architecture contains convolutional long-short term memories in order to sequentially process the input, makes use of spatial context, and captures motion. In the evaluation, we quantify improvements in estimating the velocity of braking and turning vehicles compared to the state-of-the-art. Additionally, we demonstrate that our approach provides more consistent velocity estimates for dynamic objects, as well as, less erroneous velocity estimates in static area.
Fig. 1: Object detection using deep learning and grid fusion.Abstract-We tackle the problem of object detection and pose estimation in a shared space downtown environment. For perception multiple laser scanners with 360 • coverage were fused in a dynamic occupancy grid map (DOGMa). A singlestage deep convolutional neural network is trained to provide object hypotheses comprising of shape, position, orientation and an existence score from a single input DOGMa. Furthermore, an algorithm for offline object extraction was developed to automatically label several hours of training data. The algorithm is based on a two-pass trajectory extraction, forward and backward in time. Typical for engineered algorithms, the automatic label generation suffers from misdetections, which makes hard negative mining impractical. Therefore, we propose a loss function counteracting the high imbalance between mostly static background and extremely rare dynamic grid cells. Experiments indicate, that the trained network has good generalization capabilities since it detects objects occasionally lost by the label algorithm. Evaluation reaches an average precision (AP) of 75.9 %.
Abstract-Long-term prediction of traffic participants is crucial to enable autonomous driving on public roads. The quality of the prediction directly affects the frequency of trajectory planning. With a poor estimation of the future development, more computational effort has to be put in re-planning, and a safe vehicle state at the end of the planning horizon is not guaranteed. A holistic probabilistic prediction, considering inputs, results and parameters as random variables, highly reduces the problem. A time frame of several seconds requires a probabilistic description of the scene evolution, where uncertainty or accuracy is represented by the trajectory distribution. Following this strategy, a novel evaluation method is needed, coping with the fact, that the future evolution of a scene is also uncertain. We present a method to evaluate the probabilistic prediction of real traffic scenes with varying start conditions. The proposed prediction is based on a particle filter, estimating behavior describing parameters of a microscopic traffic model. Experiments on real traffic data with random leading vehicles show the applicability in terms of convergence, enabling long-term prediction using forward propagation.
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