Torben Stolte

Automated driving systems operated at SAE levels 4 and 5 require a far-reaching fault-tolerant design. To meet this need at the actuator level, we present an integrated vehicle motion control approach that is able to tolerate a wide range of different actuator degradations as well as tire blowouts in vehicles featuring four wheel-individual steering, drive, and brake actuators. The approach, which is based on non-linear model predictive control (MPC), tracks a temporal sequence of reference poses. Fault tolerance is achieved by reconfiguration of the MPC’s constraints, weights, and prediction model, which consists of a double-track vehicle and a brush tire model. The evaluation based on the example of a demanding decelerated single lane change trajectory in IPG CarMaker demonstrates the functionality of the approach. Moreover, the example shows that the suitability of fault-tolerant vehicle motion control as part of a system-wide safety concept depends on the range of possible driving scenarios an automated driving system can encounter.

This paper presents a taxonomy that allows defining the fault tolerance regimes fail-operational , fail-degraded , and fail-safe in the context of automotive systems. Fault tolerance regimes such as these are widely used in recent publications related to automated driving, yet without definitions. This largely holds true for automotive safety standards, too. We show that fault tolerance regimes defined in scientific publications related to the automotive domain are partially ambiguous as well as taxonomically unrelated. The presented taxonomy is based on terminology stemming from ISO 26262 as well as from systems engineering. It uses four criteria to distinguish fault tolerance regimes. In addition to fail-operational , fail-degraded , and fail-safe , the core terminology consists of operational and fail-unsafe . These terms are supported by definitions of available performance , nominal performance , functionality , and a concise definition of the safe state . For verification, we show by means of two examples from the automotive domain that the taxonomy can be applied to hierarchical systems of different complexity.

The advent of automated vehicles operating at SAE levels 4 and 5 poses high fault tolerance demands for all functions contributing to the driving task. At the actuator level, fault-tolerant vehicle motion control, which exploits functional redundancies among the actuators, is one means to achieve the required degree of fault tolerance. Therefore, we give a comprehensive overview of the state of the art in actuator fault-tolerant vehicle motion control with a focus on drive, brake, and steering degradations, as well as tire blowouts. This review shows that actuator fault-tolerant vehicle motion is a widely studied field; yet, the presented approaches differ with respect to many aspects. To provide a starting point for future research, we survey the employed actuator topologies, the tolerated degradations, the presented control approaches, as well as the experiments conducted for validation. Overall, and despite the large number of different approaches, the covered literature reveals the potential of increasing fault tolerance by fault-tolerant vehicle motion control. Thus, besides developing novel approaches or demonstrating real-time applicability, future research should aim at investigating limitations and enabling comparison of fault-tolerant motion control approaches in order to allow for a thorough safety argumentation.

The complex functional structure of driverless vehicles induces a multitude of potential malfunctions. Established approaches for a systematic hazard identification generate individual potentially hazardous scenarios for each identified malfunction. This leads to inefficiencies in a purely expert-based hazard analysis process, as each of the many scenarios has to be examined individually. In this contribution, we propose an adaptation of the strategy for hazard identification for the development of automated vehicles. Instead of focusing on malfunctions, we base our process on deviations from desired vehicle behavior in selected operational scenarios analyzed in the concept phase. By evaluating externally observable deviations from a desired behavior, we encapsulate individual malfunctions and reduce the amount of generated potentially hazardous scenarios. After introducing our hazard identification strategy, we illustrate its application on one of the operational scenarios used in the research project UNICARagil.

Various manufacturers have presented concepts for autonomous vehicles in recent years. However, none of these concepts were designed specifically for the needs of a multi-generation family. A vehicle that meets these requirements can be used independently even by those who are dependent on an accompanying family member when using a conventional car. Furthermore, an autonomous family vehicle, as discussed in this paper, is intended for private use. The basis of the requirements of such a vehicle are the physical and mental abilities of its users. This paper pursues the approach of deriving initial functional requirements by considering examples of accompanied rides in a conventional car. Additionally, an outlook is given on the derivation of non-functional requirements necessary for the safe operation of the autonomous family vehicle.

Level 3+ automated driving implies highest safety demands for the entire vehicle automation functionality. For the part of trajectory tracking, functional redundancies among all available actuators provide an opportunity to reduce safety requirements for single actuators. Yet, the exploitation of functional redundancies must be well argued if employed in a safety concept as physical limits can be reached. In this paper, we want to examine from a trajectory tracking perspective whether such a concept can be used. For this, we present a model predictive fault-tolerant trajectory tracking approach for over-actuated vehicles featuring wheel individual all-wheel drive, brakes, and steering. Applying this approach exemplarily demonstrates for a selected reference trajectory that degradations such as missing or undesired wheel torques as well as reduced steering dynamics can be compensated. Degradations at the physical actuator limits lead to significant deviations from the reference trajectory while small constant steering angles are partially critical.

The project Automated Unmanned Protective Vehicle for Highway Hard Shoulder Road Works (aFAS) aims to develop an unmanned protective vehicle to reduce the risk of injuries due to crashes for road workers. To ensure functional safety during operation in public traffic the system shall be developed following the ISO 26262 standard. After defining the functional range in the item definition, a hazard analysis and risk assessment has to be done. The ISO 26262 standard gives hints how to process this step and demands a systematic way to identify system hazards. Best practice standards provide systematic ways for hazard identification, but lack applicability for automated vehicles due to the high variety and number of different driving situations even with a reduced functional range. This contribution proposes a new method to identify hazardous events for a system with a given functional description. The method utilizes a skill graph as a functional model of the system and an overall definition of a scene for automated vehicles to identify potential hazardous events. An adapted Hazard and Operability Analysis approach is used to identify system malfunctions. A combination of all methods results in operating scenes with potential hazardous events. These can be assessed afterwards towards their criticality. A use case example is taken from the current development phase of the project aFAS.

The development of fully automated vehicles imposes new challenges in the development process and during the operation of such vehicles. As traditional design methods are not sufficient to account for the huge variety of scenarios which will be encountered by (fully) automated vehicles, approaches for designing safe systems must be extended in order to allow for an ISO 26262 compliant development process. During operation of vehicles implementing SAE Levels 3+ safe behavior must always be guaranteed, as the human driver is not or not immediately available as a fall-back. Thus, the vehicle must be aware of its current performance and remaining abilities at all times. In this paper we combine insights from two research projects for showing how a skill- and ability-based approach can provide a basis for the development phase and operation of self-aware automated road vehicles.

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.