Jan Gacnik

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.

Reliable Information Aggregation and Exchange for Autonomous Vehicles

The Stadtpilot project develops a vehicular technology targeting autonomous driving in metropolitan areas. Currently, demonstrations focus on the German city of Braunschweig. These activities are further supported by the AIM test bed, which provides communication infrastructure along Braunschweigs inner ring road. Besides communication capabilities, an autonomous vehicle needs to have a complete, consistent and correct understanding about its surroundings to enable safe driving. For this purpose, this paper presents a novel approach for information aggregation and exchange through a context model tailored for urban environments. Furthermore, integration of C2X hardware into the existing traffic infrastructure is shown.

Supporting qualification: Safety standard compliant process planning and monitoring

Functional safety of embedded systems has become an integral part in automotive engineering activities due to the forthcoming safety standard ISO 26262. One main challenge is to perform development activities compliant to the standard and provide the respective documentation. Traceability between requirements from a standard, as well as project-specific process and product artifacts throughout the entire development cycle allows compliance assessment to support qualification. This paper proposes a methodology to plan and monitor the safety development process. Using a formalized requirements library of the ISO 26262 as well as a system description and its safety integrity level, a standard compliant process model is derived describing all necessary steps in the development process. Based on this process model, the methodology allows monitoring process activities and their degree of implementation, based on standard compliant confirmation measures. The main benefit is the reduced effort in preparing qualification or certification of a new safety-critical product. The development of an Adaptive Cruise Control system is sketched as an example application to illustrate the proposed proceeding.

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.

The DeSCAS Methodology and Lessons Learned on Applying Formal Reasoning to Safety Domain Knowledge

Functional safety has become an important aspect for engineering activities in the automotive domain due to the upcoming introduction of the safety standard ISO 26262. This paper proposes a methodology to guide the safety related requirements engineering process by means of OWL (Web Ontology Language) ontologies. These ontologies formalize necessary domain knowledge and serve as reference models to support semi-automated requirements discovery and to ease the certification process. Using OWL’s logical base, knowledge inference is applied to reason about safety measures for ensuring compliance with the reference process (guidance). The proposed methodology has been implemented in a prototype toolchain and applied to a simple lane departure warning system as an example assistance and automation system. Lessons learned refer to conceptual (expressiveness) and technical (tooling efficiency) issues.