Marcus Nolte

Ability and skill graphs for system modeling, online monitoring, and decision support for vehicle guidance systems

In this paper, the ability and skill graphs are introduced for modeling vehicle guidance systems in the concept phase of the development process (abilities), for online monitoring of system operation (skills), and to support driving decisions (skill levels) of automated road vehicles and advanced driver assistance systems. Both graphs rely on a decomposition of the human driving task. An ability is the entirety of conditions which are necessary to provide a certain part of the driving task. The ability graph can be developed in parallel to the item definition according to the ISO 26262 standard in the concept phase of the development process and can be used for supporting further development steps. A skill is defined as an abstract representation of a part of the driving task including information about the skills current performance. The skill graph is used to monitor the current system performance during operation and skill levels are input to driving decisions. Abilities and skills cover all aspects of the driving task including environment and self perception, data processing, decision making, and behavior execution. During operation of the developed item, the skill graph is instantiated as a (distributed) software component to process online information for assessing current skill levels. Each skill uses one or more performance metrics, which represent its current performance capability in relation to the maximum (inherent) ability level. The resulting information could replace the monitoring of the system by a human driver and can be used as an input to driving decisions of the vehicle to support appropriate and safe decisions.

Specifying a middleware for distributed embedded vehicle control systems

The software of electric / electronic vehicle control systems is static in current series vehicles. Most of the systems do not allow maintenance or functional updates, especially in the field of driver assistance systems. Main causes are the testing effort for a software release and the wide variety of different configurations in different vehicle models. In this paper we take a closer look at the requirements for a middleware which allows such updates, verifies new software versions, and adds reconfiguration mechanisms for singular control units and distributed sets of control units. To derive the requirements we consider the general vehicular context with limitations in space, electric power, processing power, and costs together with four exemplary road vehicle control applications (cruise control, automatic parking, stability control, force feedback), and a full x-by-wire target vehicle for implementing these applications. The analysis of these three different sources of requirements results in desired middleware functionalities and requirements, especially concerning runtime timings and update timings. The requirements cover an update functionality with integrated verification, the exchange of applications on singular control units, and the degradation of functionality by switching between control units.

Model predictive control based trajectory generation for autonomous vehicles — An architectural approach

Research in the field of automated driving has created promising results in the last years. Some research groups have shown perception systems which are able to capture even complicated urban scenarios in great detail. Yet, what is often missing are general-purpose path-or trajectory planners which are not designed for a specific purpose. In this paper we look at path- and trajectory planning from an architectural point of view and show how model predictive frameworks can contribute to generalized path- and trajectory generation approaches for generating safe trajectories even in cases of system failures.

Self-awareness in autonomous automotive systems

Self-awareness has been used in many research fields in order to add autonomy to computing systems. In automotive systems, we face several system layers that must be enriched with self-awareness to build truly autonomous vehicles. This includes functional aspects like autonomous driving itself, its integration on the hardware/software platform, and among others dependability, real-time, and security aspects. However, self-awareness mechanisms of all layers must be considered in combination in order to build a coherent vehicle self-awareness that does not cause conflicting decisions or even catastrophic effects. In this paper, we summarize current approaches for establishing self-awareness on those layers and elaborate why self-awareness needs to be addressed as a cross-layer problem, which we illustrate by practical examples.