Jan Schomerus

Hybrid fusion approach combining autonomous and cooperative detection and ranging methods for situation-aware driver assistance systems

Current driver assistance systems such as Adaptive Cruise Control (ACC) and in particular future assistance systems such as Collision Warning make high demands on reliability of detection and ranging methods for vehicles within the local vicinity. Autonomous systems such as Radar which are already integrated into a multitude of vehicles meet these requirements to only a limited extent. As an alternative, cooperative systems for detection and ranging will be enabled by future Vehicle-2-Vehicle communication. But cooperative detection and ranging also has drawbacks regarding reliability due to positioning and transmission errors if it is applied in a standalone way. Thus, the solution presented in this paper is a hybrid approach combining autonomous and cooperative methods for detection and ranging within a common architecture. A particle filter is used for state estimation. The results are a higher detection effectiveness and a lower position error compared to using standalone autonomous or cooperative detection and ranging methods.

Generating high precision maps for advanced guidance support

Modern driver assistance systems increasingly support safer and more fuel-efficient driving. To fulfill these tasks information about the vehicle environment is essential. Besides extracting this information from sensors to perceive the environment, it is also possible to use stored static data. These models allow a reliable and fast access to the environmental data without the typical problems faced with the recognition by sensors, like noise or glitches. Nowadays street maps produced for navigation purposes reach a precision of several meters. To support the driver during manoeuvres however a precision one magnitude better is necessary. Positioning technology has steadily improved over the years and will further improve in the near future. Alongside this development it can be expected, that more precise digital maps will gain more importance. In this paper a method is presented to create these maps mainly automatically. The primary goal for this method is to achieve the best precision possible investing a very small effort. Data is recorded using a test vehicle equipped with a navigation system featuring high precision DGPS and inertial sensors. Lateral deviations are compensated by using a image processing lane-detection sensor. Several measurement iterations are made and merged into a digital map of the street using statistical methods. In order to show the suitability of the generated map for all kinds of ADAS, a test track was surveyed and the digital map was used for automatic guidance of another test vehicle. It could be shown that the map generation algorithm is generally able to produce high precision maps even in challenging environments, however ADAS demanding precise lateral and longitudinal position information can only rely on digital maps and GNSS in environments with little or no obstruction to the sky.

Vehicle automation in cooperation with V2I and nomadic devices communication

Introduction of wireless vehicular communication allows for cooperation between vehicles and infrastructure, thus enabling a variety of new ITS use cases [1]. Furthermore, modern vehicles feature an increasing number of driving automation functions. In this paper a complex test scenario including automated driving, environment perception, communication with the infrastructure (Vehicle to Infrastructure, V2I) and with nomadic devices is described. Based on this, the requirements for a cooperative and automated ITS are identified. In order to demonstrate the test scenario, an integrated ITS design is proposed assembling Vehicle Technology and Communication Technologies with the Traffic Infrastructure and Nomadic Devices. This has been implemented in a prototype for evaluation in February 2011.