Automotive self-localization is an essential task for any automated driving function. This means that the vehicle has to reliably know its position and orientation with an accuracy of a few centimeters and degrees, respectively. This paper presents a radar-based approach to self-localization, which exploits fully polarimetric scattering information for robust landmark detection. The proposed method requires no input from sensors other than radar during localization for a given map. By association of landmark observations with map landmarks, the vehicle's position is inferred. Abstract point- and line-shaped landmarks allow for compact map sizes and, in combination with the factor graph formulation used, for an efficient implementation. Evaluation of extensive real-world experiments in diverse environments shows a promising overall localization performance of $0.12 \text{m}$ RMS absolute trajectory and $0.43 {}^\circ$ RMS heading error by leveraging the polarimetric information. A comparison of the performance of different levels of polarimetric information proves the advantage in challenging scenarios.

A new automotive radar data set with measurements and point-wise annotations from more than four hours of driving is presented. Data provided by four series radar sensors mounted on one test vehicle were recorded and the individual detections of dynamic objects were manually grouped to clusters and labeled afterwards. The purpose of this data set is to enable the development of novel (machine learning-based) radar perception algorithms with the focus on moving road users. Images of the recorded sequences were captured using a documentary camera. For the evaluation of future object detection and classification algorithms, proposals for score calculation are made so that researchers can evaluate their algorithms on a common basis. Additional information as well as download instructions can be found on the website of the data set: www.radar-scenes.com.

Radar sensors employed for environment perception, e.g. in autonomous vehicles, output a lot of unwanted clutter. These points, for which no corresponding real objects exist, are a major source of errors in following processing steps like object detection or tracking. We therefore present two novel neural network setups for identifying clutter. The input data, network architectures and training configuration are adjusted specifically for this task. Special attention is paid to the downsampling of point clouds composed of multiple sensor scans. In an extensive evaluation, the new setups display substantially better performance than existing approaches. Because there is no suitable public data set in which clutter is annotated, we design a method to automatically generate the respective labels. By applying it to existing data with object annotations and releasing its code, we effectively create the first freely available radar clutter data set representing real-world driving scenarios. Code and instructions are accessible at www.github.com/kopp-j/clutter-ds.

Radar sensors provide a unique method for executing environmental perception tasks towards autonomous driving. Especially their capability to perform well in adverse weather conditions often makes them superior to other sensors such as cameras or lidar. Nevertheless, the high sparsity and low dimensionality of the commonly used detection data level is a major challenge for subsequent signal processing. Therefore, the data points are often merged in order to form larger entities from which more information can be gathered. The merging process is often implemented in form of a clustering algorithm. This article describes a novel approach for first filtering out static background data before applying a twostage clustering approach. The two-stage clustering follows the same paradigm as the idea for data association itself: First, clustering what is ought to belong together in a low dimensional parameter space, then, extracting additional features from the newly created clusters in order to perform a final clustering step. Parameters are optimized for filtering and both clustering steps. All techniques are assessed both individually and as a whole in order to demonstrate their effectiveness. Final results indicate clear benefits of the first two methods and also the cluster merging process under specific circumstances.

The other drivers wouldn't have noticed anything unusual as the two sleek limousines with German license plates joined the traffic on France's Autoroute 1. But what they were witnessing -- on that sunny, fall day in 1994 -- was something many of them would have dismissed as just plain crazy. It had taken a few phone calls from the German car lobby to get the French authorities to give the go-ahead. But here they were: two gray Mercedes 500 SELs, accelerating up to 130 kilometers per hour, changing lanes and reacting to other cars -- autonomously, with an onboard computer system controlling the steering wheel, the gas pedal and the brakes. Decades before Google, Tesla and Uber got into the self-driving car business, a team of German engineers led by a scientist named Ernst Dickmanns had developed a car that could navigate French commuter traffic on its own. The story of Dickmann's invention, and how it came to be all but forgotten, is a neat illustration how technology sometimes progresses: not in small steady steps, but in booms and busts, in unlikely advances and inevitable retreats --"one step forward and three steps back," as one AI researcher put it. It's also a warning of sorts, about the expectations we place on artificial intelligence and the limits of some of the data-driven approaches being used today.

A synthesis of methods from cybernetics and AI yields a concept of intelligence for autonomous mobile systems that integrates closed-loop visual perception and goal-oriented action cycles using spatiotemporal models. In a layered architecture, systems dynamics methods with differential models prevail on the lower, data-intensive levels, but on higher levels, AItype methods are used. Knowledge about the world is geared to classes of objects and subjects. Subjects are defined as objects with additional capabilities of sensing, data processing, decision making, and control application. Specialist processes for visual detection and efficient tracking of class members have been developed.

Europe continues to be among the leaders in developing ground vehicles capable of real-time vision. At Bundeswehr University Munich (UniBwM), researchers are investigating "scout-type" vision for autonomous cars, which--unlike popular systems in use now--does not rely on accurate maps, GPS positioning, or databases of previously observed objects.

Chess was originally considered an exercise that captures the essential tactical and strategic elements of human intelligence, and so it became the standard by which new AI algorithms were tested. For decades, programmers made little progress in defeating human players. But in 1997, Deep Blue, a computer developed by IBM, won the match against the world champion. Still, many people were disappointed when they realized that solving chess was not the same as solving artificial general intelligence. They did not like that Deep Blue relied heavily on brute force and memory.

A synthesisof methods from cybernetics and AI yields a concept of intelligence for autonomous mobile systems that integrates closed-loop visual perception and goal-oriented action cycles using spatiotemporal models. In a layered architecture, systems dynamics methods with differential models prevail on the lower, data-intensive levels, but on higher levels, AI-type methods are used. Knowledge about the world is geared to classes of objects and subjects. Subjects are defined as objects with additional capabilities of sensing, data processing, decision making, and control application. Specialist processes for visual detection and efficient tracking of class members have been developed. On the upper levels, individual instantiations of these class members are analyzed jointly in the task context, yielding the situation for decision making. As an application, vertebrate-type vision for tasks in vehicle guidance in naturally perturbed environments was investigated with a distributed PC system. Experimental results with the test vehicle VAMORS are discussed.