Florian Kuhnt

Functional system architectures towards fully automated driving

The functional system architecture of an automated vehicle plays a crucial role in the performance of the vehicle. When considered as a backbone, it does not only transmit information between distinct layers, but rather serves as a feedback mechanism coordinating the degradation between them and thereby regulates the behavior of the system against failures. Hence, the design of robust functional architectures is essential to cope with the uncertainties of the world. This paper summarizes existing system architectures and investigates them regarding their robustness against measurement inaccuracies, failures, and unexpected evolution of traffic situations. After illustrating their strengths and deficiencies, we derive the requirements and propose a structure for future, robust system architectures.

Testing and validating high level components for automated driving: simulation framework for traffic scenarios

Current advances in the research field of autonomous driving demand advanced simulation methods for testing and validation. By combining versatile foci of different simulations, we can provide an increased amount and diversity of realistic traffic scenarios, which are relevant to the development and verification of high level automated driving functions. The focus of the present paper is to propose a concept for realistic simulation scenarios, which is capable of running in different integration levels, from software- to vehicle-in-the-loop. Its application is demonstrated, exposing an experimental vehicle, which is used for autonomous driving development, to a traffic scenario with virtual vehicles on a real road network.

DSRC and radar object matching for cooperative driver assistance systems

Dedicated Short Range Communication (DSRC) systems will become ubiquitous among vehicles in the near future. Because this technology enables communication between any set of DSRC-equipped vehicles, precise knowledge of these other vehicles is available to the host car. In addition to the DSRC system, onboard radars are able to provide high fidelity dynamics measurements of other objects within the sensing range. Given these two methods of measurement, environmental perception for driver assistance systems can be greatly improved, especially if the measurements are fused together. However, this is not a trivial task because of an inherent data association problem: Given the objects detected by the radar sensor, which one is truly the DSRC message sender? In this paper, we propose a system architecture to fuse DSRC and radar data. This architecture uses a reliable statistical track-to-track association algorithm in a novel way to solve this data matching problem. We present experimental results of this architecture on a system running in real traffic situations in the U. S.

Particle Filter Map Matching and Trajectory Prediction Using a Spline Based Intersection Model

For Advanced Driver Assistance Systems and Autonomous Driving it is of major advantage to know future trajectories of traffic participants. These are influenced by many factors in the environment. One important factor is the geometry of the intersection a vehicle is approaching. In this paper we describe how we can extract a spline based intersection model from low detail map data like Open-StreetMap that can be adjusted over time. A particle filter based map matching algorithm is used to localize the ego vehicle relative to the intersection model. Additionally, objects detected from the ego vehicles sensors are matched onto the intersection model in order to predict the future trajectories of the ego vehicle and other traffic participants using the intersection model.

Lane-precise localization of intelligent vehicles using the surrounding object constellation

Advanced Driver Assistance Systems (ADAS) towards autonomous driving require an ego vehicle localization on maps to be able to use the map data for e.g. behavior and trajectory prediction of traffic participants.

Autonomous multi-story navigation for valet parking

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 efficient autonomous navigation for vehicles in gps-denied environments like 3-D multi-story parking garages. In addition a robot is employed to autonomously charge the parked electric vehicles. We give insight into the concept and implementation of such a system, and evaluate it in real parking garages. We extensively tested the system in a real-world application, where a driver leaves the vehicle at the entry of a parking garage and the vehicle then performs the navigation and parking task on its own. Our test vehicle autonomously navigated more than 50 times from the entry of a parking garage to an assigned parking spot on the 6th floor and docked with the charging robot. The navigation system is precise, efficient and capable of running online in real-world scenarios.

Simulation framework for the development of autonomous small scale vehicles

The development and validation of highly automated driving functions towards autonomous driving requires efficient frameworks to reduce the necessity of expensive, time-consuming and dangerous real test drives. This applies for the development of autonomous small scale as well as real scale vehicles. In the present work we introduce a simulation framework for the development and virtual validation of small scale vehicles to tackle this issue. At the concrete challenge of the student competition AUDI Autonomous Driving Cup, we demonstrate how a closed world can be transformed into appropriate simulation models in order to stimulate higher level automated driving functions. The framework is demonstrated at the example of highly automated driving functions on a small scale vehicle. At the end, we transfer the important results to the virtual validation of real scale autonomous vehicles.

Robust environment perception for the Audi Autonomous Driving Cup

One of the biggest challenges towards fully automated driving is achieving robustness. Autonomous vehicles will have to fully recognize their environment even in harsh weather conditions. Additionally, they have to be able to detect sensor and algorithm failures and react properly to keep the vehicle in a safe state.

Performance evaluation and statistical analysis of algorithms for ego-motion estimation

This contribution investigates algorithms for egomotion estimation from environmental features. Various formulations for solving the underlying procrustes problem exist. It is analytically shown that in the 2-D case this can be performed more efficiently compared to common implementations based on matrix decompositions. Furthermore, analytic error propagation is performed to second order which reveals a multiplicative estimator bias. A novel bias-corrected solution is proposed and evaluated in Monte Carlo simulations. Propagation of the derived error model to a representation used in the recursive trajectory reconstruction is presented and verified.