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

Predicting spatial spread of rabies in skunk populations using surveillance data reported by the public

Background: Prevention and control of wildlife disease invasions relies on the ability to predict spatio-temporal dynamics and understand the role of factors driving spread rates, such as seasonality and transmission distance. Passive disease surveillance (i.e., case reports by public) is a common method of monitoring emergence of wildlife diseases, but can be challenging to interpret due to spatial biases and limitations in data quantity and quality. Methodology/Principal findings: We obtained passive rabies surveillance data from dead striped skunks (Mephitis mephitis) in an epizootic in northern Colorado, USA. We developed a dynamic patch-occupancy model which predicts spatio-temporal spreading while accounting for heterogeneous sampling. We estimated the distance travelled per transmission event, direction of invasion, rate of spatial spread, and effects of infection density and season. We also estimated mean transmission distance and rates of spatial spread using a phylogeographic approach on a subsample of viral sequences from the same epizootic. Both the occupancy and phylogeographic approaches predicted similar rates of spatio-temporal spread. Estimated mean transmission distances were 2.3 km (95% Highest Posterior Density (HPD95): 0.02, 11.9; phylogeographic) and 3.9 km (95% credible intervals (CI95): 1.4, 11.3; occupancy). Estimated rates of spatial spread in km/year were: 29.8 (HPD95: 20.8, 39.8; phylogeographic, branch velocity, homogenous model), 22.6 (HPD95: 15.3, 29.7; phylogeographic, diffusion rate, homogenous model) and 21.1 (CI95: 16.7, 25.5; occupancy). Initial colonization probability was twice as high in spring relative to fall. Conclusions/Significance: Skunk-to-skunk transmission was primarily local (< 4 km) suggesting that if interventions were needed, they could be applied at the wave front. Slower viral invasions of skunk rabies in western USA compared to a similar epizootic in raccoons in the eastern USA implies host species or landscape factors underlie the dynamics of rabies invasions. Our framework provides a straightforward method for estimating rates of spatial spread of wildlife diseases

Context Exploitation in Data Fusion

Complex and dynamic environments constitute a challenge for existing tracking algorithms. For this reason, modern solutions are trying to utilize any available information which could help to constrain, improve or explain the measurements. So called Context Information (CI) is understood as information that surrounds an element of interest, whose knowledge may help understanding the (estimated) situation and also in reacting to that situation. However, context discovery and exploitation are still largely unexplored research topics. Until now, the context has been extensively exploited as a parameter in system and measurement models which led to the development of numerous approaches for the linear or non-linear constrained estimation and target tracking. More specifically, the spatial or static context is the most common source of the ambient information, i.e. features, utilized for recursive enhancement of the state variables either in the prediction or the measurement update of the filters. In the case of multiple model estimators, context can not only be related to the state but also to a certain mode of the filter. Common practice for multiple model scenarios is to represent states and context as a joint distribution of Gaussian mixtures. These approaches are commonly referred as the join tracking and classification. Alternatively, the usefulness of context was also demonstrated in aiding the measurement data association. Process of formulating a hypothesis, which assigns a particular measurement to the track, is traditionally governed by the empirical knowledge of the noise characteristics of sensors and operating environment, i.e. probability of detection, false alarm, clutter noise, which can be further enhanced by conditioning on context. We believe that interactions between the environment and the object could be classified into actions, activities and intents, and formed into structured graphs with contextual links translated into arcs. By learning the environment model we will be able to make prediction on the target\u2019s future actions based on its past observation. Probability of target future action could be utilized in the fusion process to adjust tracker confidence on measurements. By incorporating contextual knowledge of the environment, in the form of a likelihood function, in the filter measurement update step, we have been able to reduce uncertainties of the tracking solution and improve the consistency of the track. The promising results demonstrate that the fusion of CI brings a significant performance improvement in comparison to the regular tracking approaches

Pedestrian Prediction by Planning using Deep Neural Networks

Accurate traffic participant prediction is the prerequisite for collision avoidance of autonomous vehicles. In this work, we predict pedestrians by emulating their own motion planning. From online observations, we infer a mixture density function for possible destinations. We use this result as the goal states of a planning stage that performs motion prediction based on common behavior patterns. The entire system is modeled as one monolithic neural network and trained via inverse reinforcement learning. Experimental validation on real world data shows the system's ability to predict both, destinations and trajectories accurately

Environment Perception for Driver Assistance Systems Using Hierarchical Occupancy Grids

Die Zahl der im Straßenverkehr Verunglückten in Deutschland ist rückläufig. Die Fahrzeughersteller tragen ihren Teil zur Sicherheit im Straßenverkehr bei, indem sie Fahrern immer komplexere Fahrerassistenz- und Sicherheitssysteme anbieten, um sicher und entspannt ans Ziel zu kommen. Für moderne Fahrerassistenzsysteme ist eine zuverlässige und umfassende Umgebungserfassung unerlässlich. Um möglichst viele unterschiedliche Systeme mit einer gemeinsamen Umgebungserfassung bedienen zu können, ist es notwendig, dass diese eine möglichst sensorunabhängige, anwendungsübergreifende, konsistente und umfassende Repräsentation der Umgebung liefert. Belegungskarten ermöglichen die Analyse von Freiraum, die Fahrbahnverlaufsschätzung, die Detektion bewegter Objekte und die Selbstlokalisierung. In dieser Arbeit wird daher eine dreidimensionale Belegungskarte zur Umgebungsmodellierung für Fahrerassistenzsysteme eingesetzt. Um den effizienten Einsatz von dreidimensionalen Karten zu ermöglichen wird ein Verfahren vorgestellt, mit dem sich der Detaillierungsgrad der Umgebungsmodellierung dynamisch und anwendungsgesteuert anpassen lässt. Um die Umgebungserfassung zu vervollständigen wird sowohl die rasterbasierte Selbstlokalisierung in zwei und drei Dimensionen als auch die Detektion und Verfolgung bewegter Objekte behandelt. Als weitere Anwendungen für die vorgeschlagene Umgebungsrepräsentation werden eine Laserscanner-basierte Höhenschätzung für Brücken und eine Parklückendetektion und -vermessung beschrieben und evaluiert.The number of casualties from road traffic is decreasing in Germany. Vehicle manufacturers contribute to safety by providing drivers with more and more complex driver assistance and safety systems, making driving safer and more relaxing. A reliable and comprehensive environment perception is essential for modern driver assistance systems. To support as many different applications as possible with a single environment representation, a sensor-independent and application-independent consistent environment representation must be applied. Occupancy grids enable free space analysis, road course estimation, detection of moving objects and self-localization among other things. Therefore, this thesis uses a three-dimensional occupancy grid to model the environment for driver assistance systems. To allow for an efficient application of three-dimensional occupancy grids, a dynamic method to control the occupancy grid detail level is proposed. The desired resolution is controlled by the applications. To complete the environment perception, a grid-based simultaneous self-localization and mapping approach for two and three dimensions as well as the detection and tracking of moving objects are described and evaluated. In addition, exemplary applications for the proposed environment representation are characterized and analyzed: lidar-based height estimation for bridges and parking spot detection and measuring