Abstract

Modern cars are equipped with a variety of sensors, advanced driver assistance systems and user interfaces nowadays. To benefit from these systems and to optimally support the driver in his monitoring and decision making process, efficient human-machine interfaces play an important part. This paper describes the second release of iDriver, an iPad software solution which was developed to navigate and remote control autonomous cars, to give access to live sensor data and useful data about the car state, as there are, e.g., current speed, engine and gear state. The software was used and evaluated in our two fully autonomous research cars “Spirit of Berlin” and “Made in Germany”.

Abstract

Nowadays, driver assistance systems involve an ever increasing degree of automatization. Costly effort is put into testing the individual components to ensure proper functioning by the vehicle manufacturers. However, problems can also arise on a macroscopic scale, as vehicles and infrastructure are recently equipped with short range radio communication (“Car2X”). These problems caused by interaction are of even greater concern than “normal” bugs, as the final product might already have been deployed when the issues first become apparent. Multi-Agent System (MAS) research refers to such issues as “emergent misbehavior”. The said field also brought up an approach to automatically discover the worst consequences of the malfunctions. Hence, a given system under test can already be revised during development, saving a tremendous amount of resources. The approach from MAS is adapted to the domain of testing driver assistance systems in traffic simulations. A green-light optimal-speed advisory (GLOSA) algorithm is used as an example in which conceptual problems are discovered by the testing system after simpler issues are eliminated.

Abstract

Vehicular ad hoc networks (VANETs), often called Car2X communication systems, are about to enter the mass market in upcoming years. They are intended to increase traffic safety by enabling new safety critical driver assistance systems. This also means that strong security mechanisms are required to safeguard communication within VANETs. However, standardized security mechanisms lead to significant overhead in terms of data rate requirement and delay. Prior work has focused on reducing the overhead by advanced strategies for pseudonym and authorization authority certificate exchange. However, we find that this is not enough to enable reliable message exchange in VANETs. Various other sources of overhead caused by security mechanisms in VANETs are identified in the provided analysis. Thereby, we find cross layer and cross message dependencies. In combination with the non-fragmentation property of VANET messages, such dependencies are discovered to lead to massive dropping of packets due to maximum size violations at low protocol layers. Thus, we develop a method for cross layer on demand content assembling for VANET messages, which can avoid the size limit violations without preventing individual layers from disseminating their variable length data sets.

Abstract

The objective of this project is to develop a methodology for the design, testing, evaluation and implementation of control systems for Advanced Driver Assistance Systems (ADAS). Examples of ADAS are collision avoidance systems, lane departure warning systems, pre-crash sensing, and adaptive cruise control [1]. In this project we focus on the fault-tolerant control of ADAS. Up to now efficient and cost-effective methodology for designing ADAS control systems with sufficient capability for analyzing and verifying the fault-management of these systems could be applied. The aim of this project is to develop such a methodology.