Long-Term On-Board Prediction of People in Traffic Scenes under Uncertainty

Progress towards advanced systems for assisted and autonomous driving is leveraging recent advances in recognition and segmentation methods. Yet, we are still facing challenges in bringing reliable driving to inner cities, as those are composed of highly dynamic scenes observed from a moving platform at considerable speeds. Anticipation becomes a key element in order to react timely and prevent accidents. In this paper we argue that it is necessary to predict at least 1 second and we thus propose a new model that jointly predicts ego motion and people trajectories over such large time horizons. We pay particular attention to modeling the uncertainty of our estimates arising from the non-deterministic nature of natural traffic scenes. Our experimental results show that it is indeed possible to predict people trajectories at the desired time horizons and that our uncertainty estimates are informative of the prediction error. We also show that both sequence modeling of trajectories as well as our novel method of long term odometry prediction are essential for best performance.Comment: CVPR 201

Multiple path prediction for traffic scenes using LSTMs and mixture density models

This work presents an analysis of predicting multiple future paths of moving objects in traffic scenes by leveraging Long Short-Term Memory architectures (LSTMs) and Mixture Density Networks (MDNs) in a single-shot manner. Path prediction allows estimating the future positions of objects. This is useful in important applications such as security monitoring systems, Autonomous Driver Assistance Systems and assistive technologies. Normal approaches use observed positions (tracklets) of objects in video frames to predict their future paths as a sequence of position values. This can be treated as a time series. LSTMs have achieved good performance when dealing with time series. However, LSTMs have the limitation of only predicting a single path per tracklet. Path prediction is not a deterministic task and requires predicting with a level of uncertainty. Predicting multiple paths instead of a single one is therefore a more realistic manner of approaching this task. In this work, predicting a set of future paths with associated uncertainty was archived by combining LSTMs and MDNs. The evaluation was made on the KITTI and the CityFlow datasets on three type of objects, four prediction horizons and two different points of view (image coordinates and birds-eye vie

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

Egocentric Vision-based Future Vehicle Localization for Intelligent Driving Assistance Systems

Predicting the future location of vehicles is essential for safety-critical applications such as advanced driver assistance systems (ADAS) and autonomous driving. This paper introduces a novel approach to simultaneously predict both the location and scale of target vehicles in the first-person (egocentric) view of an ego-vehicle. We present a multi-stream recurrent neural network (RNN) encoder-decoder model that separately captures both object location and scale and pixel-level observations for future vehicle localization. We show that incorporating dense optical flow improves prediction results significantly since it captures information about motion as well as appearance change. We also find that explicitly modeling future motion of the ego-vehicle improves the prediction accuracy, which could be especially beneficial in intelligent and automated vehicles that have motion planning capability. To evaluate the performance of our approach, we present a new dataset of first-person videos collected from a variety of scenarios at road intersections, which are particularly challenging moments for prediction because vehicle trajectories are diverse and dynamic.Comment: To appear on ICRA 201

Long-term future prediction under uncertainty and multi-modality

Humans have an innate ability to excel at activities that involve prediction of complex object dynamics such as predicting the possible trajectory of a billiard ball after it has been hit by the player or the prediction of motion of pedestrians while on the road. A key feature that enables humans to perform such tasks is anticipation. There has been continuous research in the area of Computer Vision and Artificial Intelligence to mimic this human ability for autonomous agents to succeed in the real world scenarios. Recent advances in the field of deep learning and the availability of large scale datasets has enabled the pursuit of fully autonomous agents with complex decision making abilities such as self-driving vehicles or robots. One of the main challenges encompassing the deployment of these agents in the real world is their ability to perform anticipation tasks with at least human level efficiency. To advance the field of autonomous systems, particularly, self-driving agents, in this thesis, we focus on the task of future prediction in diverse real world settings, ranging from deterministic scenarios such as prediction of paths of balls on a billiard table to the predicting the future of non-deterministic street scenes. Specifically, we identify certain core challenges for long-term future prediction: long-term prediction, uncertainty, multi-modality, and exact inference. To address these challenges, this thesis makes the following core contributions. Firstly, for accurate long-term predictions, we develop approaches that effectively utilize available observed information in the form of image boundaries in videos or interactions in street scenes. Secondly, as uncertainty increases into the future in case of non-deterministic scenarios, we leverage Bayesian inference frameworks to capture calibrated distributions of likely future events. Finally, to further improve performance in highly-multimodal non-deterministic scenarios such as street scenes, we develop deep generative models based on conditional variational autoencoders as well as normalizing flow based exact inference methods. Furthermore, we introduce a novel dataset with dense pedestrian-vehicle interactions to further aid the development of anticipation methods for autonomous driving applications in urban environments.Menschen haben die angeborene Fähigkeit, Vorgänge mit komplexer Objektdynamik vorauszusehen, wie z. B. die Vorhersage der möglichen Flugbahn einer Billardkugel, nachdem sie vom Spieler gestoßen wurde, oder die Vorhersage der Bewegung von Fußgängern auf der Straße. Eine Schlüsseleigenschaft, die es dem Menschen ermöglicht, solche Aufgaben zu erfüllen, ist die Antizipation. Im Bereich der Computer Vision und der Künstlichen Intelligenz wurde kontinuierlich daran geforscht, diese menschliche Fähigkeit nachzuahmen, damit autonome Agenten in der realen Welt erfolgreich sein können. Jüngste Fortschritte auf dem Gebiet des Deep Learning und die Verfügbarkeit großer Datensätze haben die Entwicklung vollständig autonomer Agenten mit komplexen Entscheidungsfähigkeiten wie selbstfahrende Fahrzeugen oder Roboter ermöglicht. Eine der größten Herausforderungen beim Einsatz dieser Agenten in der realen Welt ist ihre Fähigkeit, Antizipationsaufgaben mit einer Effizienz durchzuführen, die mindestens der menschlichen entspricht. Um das Feld der autonomen Systeme, insbesondere der selbstfahrenden Agenten, voranzubringen, konzentrieren wir uns in dieser Arbeit auf die Aufgabe der Zukunftsvorhersage in verschiedenen realen Umgebungen, die von deterministischen Szenarien wie der Vorhersage der Bahnen von Kugeln auf einem Billardtisch bis zur Vorhersage der Zukunft von nicht-deterministischen Straßenszenen reichen. Insbesondere identifizieren wir bestimmte grundlegende Herausforderungen für langfristige Zukunftsvorhersagen: Langzeitvorhersage, Unsicherheit, Multimodalität und exakte Inferenz. Um diese Herausforderungen anzugehen, leistet diese Arbeit die folgenden grundlegenden Beiträge. Erstens: Für genaue Langzeitvorhersagen entwickeln wir Ansätze, die verfügbare Beobachtungsinformationen in Form von Bildgrenzen in Videos oder Interaktionen in Straßenszenen effektiv nutzen. Zweitens: Da die Unsicherheit in der Zukunft bei nicht-deterministischen Szenarien zunimmt, nutzen wir Bayes’sche Inferenzverfahren, um kalibrierte Verteilungen wahrscheinlicher zukünftiger Ereignisse zu erfassen. Drittens: Um die Leistung in hochmultimodalen, nichtdeterministischen Szenarien wie Straßenszenen weiter zu verbessern, entwickeln wir tiefe generative Modelle, die sowohl auf konditionalen Variations-Autoencodern als auch auf normalisierenden fließenden exakten Inferenzmethoden basieren. Darüber hinaus stellen wir einen neuartigen Datensatz mit dichten Fußgänger-Fahrzeug- Interaktionen vor, um Antizipationsmethoden für autonome Fahranwendungen in urbanen Umgebungen weiter zu entwickeln

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