Felix Fahrenkrog

Validation and Verification of Automated Road Vehicles

Ubiquitous, commercial deployment of automated road vehicles is desirable in order to realize their potential benefits such as crash avoidance, congestion mitigation, reduced environment impact, reduced driver stress, and increased driver productivity. A rigorous application of systems engineering, which includes validation and verification as crucial elements of assurance, is needed for the design and development of automated road vehicles. We discuss, without implying any form of joint recommendation, several areas of relevance to a common understanding of validation and verification of automated vehicles, namely customer expectations for vehicle response, industry standards for terms and definitions, industry standards for how measurement should be done, deeper knowledge of driving behavior today to serve as a reference, and standardized processes that encompass minimum performance requirements.

Technical Evaluation and Impact Assessment of Automated Driving

Currently different research activities on automated driving are conducted around the globe. The European flagship research project on automated driving functions is the research project “AdaptIVe” (Automated Driving Applications and Technologies for Intelligent Vehicles). Besides the development of automated driving functions, the project deals with general research on legal aspects, human factors and evaluation. The evaluation and impact assessment of automated driving functions faces different challenges considering the complexity of the technology. In this context, this paper describes the evaluation approaches that are taken in the project for the technical evaluation and impact assessment.

Evaluation of Automated Road Vehicles

In the past years various partially automated driving functions have been introduced on the market. More advanced functions are currently in research. By means of these functions partly automated driving in specific driving situations is already realized, e.g. a traffic jam assist performs longitudinal and lateral control at low speeds. Besides the technical challenges to realize automated driving in complex driving situations, e.g. intersection areas, new approaches to evaluate these functions under different driving conditions are necessary, in order to assess the benefits and identify potential weaknesses. In this context, this chapter describes a systematic approach to evaluate road vehicles with automated driving functions. The presented approach is divided into a technical, a user-related and an impact evaluation.

SIMPATO - the Safety Impact Assessment Tool of interactIVe

One step in the development of safety oriented Advanced Driver Assistance Systems is an ex ante assessment of the expected safety impacts. This requires a careful analysis combining models and data from various sources. This paper describes the Safety IMPact Assessment Tool, called SIMPATO, that was developed in the interactIVe project.

Cognitive Driver Behavior Modeling: Influence of Personality and Driver Characteristics on Driver Behavior

The prospective safety impact assessment is an important method for evaluating traffic safety effects of Advanced Driving Assistance Systems (ADAS). Different approaches are known to determine a technology’s impact on traffic safety, e.g. driving simulator studies, driving studies on the test track, Field Operational Tests and virtual simulation. For the virtual simulation, it is essential to have a detailed representation of the interactive processes between the driver, the vehicle with the system under assessment and the environment in traffic. The modeling of the driver led to the development of the Stochastic Cognitive Model (SCM) at the BMW Group that bases on stochastic processes and considers human cognitive behavior. This paper focuses on driver behavior modeling. For this purpose, the paper investigates in detail the influence of driver characteristics and personality on driver behavior in a virtual multi-agent simulation based on a driving simulator study. Therefore, correlations between driver characteristics and relevant aspects of driver behavior have been investigated and results will be reported in the present paper.