Florian Wirth

Bio: Florian Wirth is an academic researcher from Karlsruhe Institute of Technology. The author has contributed to research in topics: Feature (machine learning) & Computer science. The author has an hindex of 4, co-authored 10 publications receiving 134 citations.

Pedestrian Prediction by Planning Using Deep Neural Networks

Abstract: Accurate traffic participant prediction is the prerequisite for collision avoidance of autonomous vehicles. In this work, we propose to predict pedestrians using goal-directed planning. For this, we infer a mixture density function for possible destinations. We use these destinations as the goal states of a planning stage that performs motion prediction based on common behavior patterns. The patterns are learned by a fully convolutional network operating on maps of the environment. We show that this entire system can be 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.

Pedestrian Prediction by Planning using Deep Neural Networks

Abstract: 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.

PointAtMe: Efficient 3D Point Cloud Labeling in Virtual Reality

Abstract: Generating annotations which can be used to train new models has become an independent field of research within machine learning. Its goal is producing highly accurate annotations as cost efficient as possible. 3D point clouds are the common sensor output when recording 3D data from a mobile platform. The latest ways of annotating 3D point clouds include their visualization on a 2D screen. This method contradicts the goal of time-efficient annotating since it is unintuitive and therefore unnecessarily time consuming. We present a novel labeling technique in Virtual Reality. Using our tool, we accelerate the process of data annotation significantly compared to existing approaches. Furthermore, we will give the machine learning community access to our tool and create a new community-labeled dataset for autonomous driving. Furthermore we plan to set up an annotation benchmark in which primarily commercial annotation companies but also researchers active in annotation can take part in. We present results from an experimental plattform based on Oculus Rift indicating a huge potential for VR annotations.

RNN-based Pedestrian Crossing Prediction using Activity and Pose-related Features

Abstract: Pedestrian crossing prediction is a crucial task for autonomous driving. Numerous studies show that an early estimation of the pedestrian's intention can decrease or even avoid a high percentage of accidents. In this paper, different variations of a deep learning system are proposed to attempt to solve this problem. The proposed models are composed of two parts: a CNN-based feature extractor and an RNN module. All the models were trained and tested on the JAAD dataset. The results obtained indicate that the choice of the features extraction method, the inclusion of additional variables such as pedestrian gaze direction and discrete orientation, and the chosen RNN type have a significant impact on the final performance.

RNN-based Pedestrian Crossing Prediction using Activity and Pose-related Features

Abstract: Pedestrian crossing prediction is a crucial task for autonomous driving. Numerous studies show that an early estimation of the pedestrian's intention can decrease or even avoid a high percentage of accidents. In this paper, different variations of a deep learning system are proposed to attempt to solve this problem. The proposed models are composed of two parts: a CNN-based feature extractor and an RNN module. All the models were trained and tested on the JAAD dataset. The results obtained indicate that the choice of the features extraction method, the inclusion of additional variables such as pedestrian gaze direction and discrete orientation, and the chosen RNN type have a significant impact on the final performance.

Human motion trajectory prediction: a survey:

Abstract: 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. Spec...

Human Motion Trajectory Prediction: A Survey

Abstract: 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.

TNT: Target-driveN Trajectory Prediction

Abstract: Predicting the future behavior of moving agents is essential for real world applications. It is challenging as the intent of the agent and the corresponding behavior is unknown and intrinsically multimodal. Our key insight is that for prediction within a moderate time horizon, the future modes can be effectively captured by a set of target states. This leads to our target-driven trajectory prediction (TNT) framework. TNT has three stages which are trained end-to-end. It first predicts an agent's potential target states $T$ steps into the future, by encoding its interactions with the environment and the other agents. TNT then generates trajectory state sequences conditioned on targets. A final stage estimates trajectory likelihoods and a final compact set of trajectory predictions is selected. This is in contrast to previous work which models agent intents as latent variables, and relies on test-time sampling to generate diverse trajectories. We benchmark TNT on trajectory prediction of vehicles and pedestrians, where we outperform state-of-the-art on Argoverse Forecasting, INTERACTION, Stanford Drone and an in-house Pedestrian-at-Intersection dataset.

PIE: A Large-Scale Dataset and Models for Pedestrian Intention Estimation and Trajectory Prediction

Abstract: Pedestrian behavior anticipation is a key challenge in the design of assistive and autonomous driving systems suitable for urban environments. An intelligent system should be able to understand the intentions or underlying motives of pedestrians and to predict their forthcoming actions. To date, only a few public datasets were proposed for the purpose of studying pedestrian behavior prediction in the context of intelligent driving. To this end, we propose a novel large-scale dataset designed for pedestrian intention estimation (PIE). We conducted a large-scale human experiment to establish human reference data for pedestrian intention in traffic scenes. We propose models for estimating pedestrian crossing intention and predicting their future trajectory. Our intention estimation model achieves 79% accuracy and our trajectory prediction algorithm outperforms state-of-the-art by 26% on the proposed dataset. We further show that combining pedestrian intention with observed motion improves trajectory prediction. The dataset and models are available at http://data.nvision2.eecs.yorku.ca/PIE_dataset/.