Michael Weber

Reliable traffic light detection is one crucial key component for autonomous driving in urban areas. This includes the extraction of direction arrows contained within the traffic lights as an autonomous car will need this information for selecting the traffic light corresponding to its current lane. Current state of the art traffic light detection...

Current state-of-the-art object detection algorithms still suffer the problem of imbalanced distribution of training data over object classes and background. Recent work introduced a new loss function called focal loss to mitigate this problem, but at the cost of an additional hyperparameter. Manually tuning this hyperparameter for each training ta...

In autonomous driving, the detection of objects like surrounding vehicles based on monocular RGB images is usually performed by 2D bounding box detectors. The resulting 2D objects can be used for a first coarse 3D position estimate but for a precise location, additional sensor data has to be taken into account. For further use in sensor fusion syst...

Taking into account information across the temporal domain helps to improve environment perception in autonomous driving. However, it has not been studied so far whether temporally fused neural networks are vulnerable to deliberately generated perturbations, i.e. adversarial attacks, or whether temporal history is an inherent defense against them....

Taking into account information across the temporal domain helps to improve environment perception in autonomous driving. However, it has not been studied so far whether temporally fused neural networks are vulnerable to deliberately generated perturbations, i.e. adversarial attacks, or whether temporal history is an inherent defense against them....

The mixture-of-experts (MoE) architecture is an approach to aggregate several expert components via an additional gating module, which learns to predict the most suitable distribution of the expert’s outputs for each input. An MoE thus not only relies on redundancy for increased robustness—we also demonstrate how this architecture can provide addit...

Deployment of modern data-driven machine learning methods, most often realized by deep neural networks (DNNs), in safety-critical applications such as health care, industrial plant control, or autonomous driving is highly challenging due to numerous model-inherent shortcomings. These shortcomings are diverse and range from a lack of generalization...

The use of deep neural networks (DNNs) in safety-critical applications like mobile health and autonomous driving is challenging due to numerous model-inherent shortcomings. These shortcomings are diverse and range from a lack of generalization over insufficient interpretability to problems with malicious inputs. Cyber-physical systems employing DNN...

For reliable environment perception, the use of temporal information is essential in some situations. Especially for object detection, sometimes a situation can only be understood in the right perspective through temporal information. Since image-based object detectors are currently based almost exclusively on CNN architectures, an extension of the...

Not only correct scene understanding, but also ability to understand the decision making process of neural networks is essential for safe autonomous driving. Current work mainly focuses on uncertainty measures, often based on Monte Carlo dropout, to gain at least some insight into a models confidence. We investigate a mixture of experts architectur...

This paper presents the concept, realization and evaluation of a flexible and scalable setup for smart infrastructure at the example of the Test Area Autonomous Driving Baden-Württemberg.

We propose an integrated approach of combining end-to-end learned trajectory proposals with a probabilistic sampling based planning algorithm for autonomous driving. A convolutional neural network is trained based on monocular image data to predict prospective steering angles. By using a local history of image data, we achieve an implicit spatial r...

Making Convolutional Neural Networks (CNNs) successful in learning problems like image based ego motion estimation, highly depends on the ability of the network to extract the temporal information from videos. Therefore, the architecture of a network needs the capability to learn temporal features. We propose two CNN architectures which are able to...

Navigation and obstacle avoidance are two problems that are not easily incorporated into direct control of autonomous vehicles solely based on visual input. However, they are required if lane following given proper lane markings is not enough to incorporate trained systems into larger architectures. This paper presents a method to allow for obstacl...

In this paper, we present a method to estimate abstract parameters of high definition (HD) maps from sensor data. Parameters we estimate include the distance from ego-vehicle to road boundary, orientation of the ego-vehicle with respect to lanes, number of lanes, and street type. Our method is realized as a Convolutional Neural Network (CNN) that t...

Grid maps are widely used in robotics to represent obstacles in the environment and differentiating dynamic objects from static infrastructure is essential for many practical applications. In this work, we present a methods that uses a deep convolutional neural network (CNN) to infer whether grid cells are covering a moving object or not. Compared...

We present a reinforcement learning approach using Deep Q-Networks to steer a vehicle in a 3D physics simulation. Relying solely on camera image input the approach directly learns steering the vehicle in an end-to-end manner. The system is able to learn human driving behavior without the need of any labeled training data. An action-based reward fun...

While most approaches to semantic reasoning have focused on improving performance, in this paper we argue that computational times are very important in order to enable real time applications such as autonomous driving. Towards this goal, we present an approach to joint classification, detection and semantic segmentation via a unified architecture...

Spiking neural networks are in theory more computationally powerful than rate-based neural networks often used in deep learning architectures. However, unlike rate-based neural networks, it is yet unclear how to train spiking networks to solve complex problems. There are still no standard algorithms and it is preventing roboticists to use spiking n...

Electric mobility combined with recent advances in autonomous driving provides a solution to the environmental and traffic challenges of the modern metropolis. In this work we present an innovative system that completely changes valet parking and the process of charging electric vehicles. The introduced system tackles the problem of precise and eff...

Reliable real-time detection of traffic lights is a major concern for the task of autonomous driving. As deep convolutional networks have proven to be a powerful tool in visual object detection, we propose DeepTLR, a camera-based system for real-time detection and classification of traffic lights. Detection and state classification are realized usi...

Recent advances have shown that clothing appearance provides important features for person re-identification and retrieval in surveillance and multimedia data. However, the regions from which such features are extracted are usually only very crudely segmented, due to the difficulty of segmenting highly articulated entities such as persons. In order...