Marc René Zofka

Semivirtual simulations for the evaluation of vision-based ADAS

The design and development process of advanced driver assistance systems (ADAS) is divided into different phases, where the algorithms are implemented as a model, then as software and finally as hardware. Since it is unfeasable to simulate all possible driving situations for environmental perception and interpretation algorithms, there is still a need for expensive and time-consuming real test drives of thousands of kilometers. Therefore we present a novel approach for testing and evaluation of vision-based ADAS, where reliable simulations are fused with recorded data from test drives to provide a task-specific reference model. This approach provides ground truth with much higher reliability and reproducability than real test drives and authenticity than using pure simulations and can be applied already in early steps of the design process. We illustrate the effectiveness of our approach by testing a vision-based collision mitigation system on recordings of a german highway.

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.

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.

Making Bertha Cooperate–Team AnnieWAY’s Entry to the 2016 Grand Cooperative Driving Challenge

This paper presents the concepts and methods utilized by Team AnnieWAY for the 2016 Grand Cooperative Driving Challenge. The paper introduces the automated vehicle BerthaOne. The vehicle, even though being based on the Bertha platform, distinguishes itself from its siblings by its software modules and algorithms. We, therefore, describe its system architecture and algorithms for perception, cooperation and motion planning. In Particular, we present a motion planner that plans different maneuvers flexibly by augmenting the cost function with situation specific cost terms. We subsequently describe the requirements of the 2016 GCDC and evaluate our performance during the competition.

Robust, Marker-Based Head Tracking for Testing Cognitive Vehicles in the Loop

One of the great challenges for the release of automated vehicles is the verification and evaluation of their cognitive skills. Therefore, Vehicle-in-the-loop testing provides an adequate tool, combining real test drives with simulations. In this paper we present our innovative Vehicle-in-the-loop framework: First, a marker-based head tracking is presented, which is able to track the driver’s head in presence of vehicle’s movement and illumination influences. Second, we present our realization based on the Open-Source framework ROS with an exemplary coupling to the professional simulation tool CarMaker. Finally, we evaluate our approach in different driving scenarios.