100 research outputs found
Safe Transitions From Automated to Manual Driving Using Driver Controllability Estimation
No full textIn this paper, we consider the problem of assessing when the control of a vehicle can be safely transferred from an automated driving system to the driver. We propose a method based on a description of the driver\u27s capabilities to maneuver the vehicle, which is defined as a subset of the vehicle\u27s state space and called the driver controllability set (DCS). Since drivers\u27 capabilities vary among individuals, the DCS is updated online during manual driving. By identifying the limits of the individual driver\u27s normal driving envelope, we find the estimated bounds of the DCS. Using a vehicle model and reachability analysis, we assess whether the states of the vehicle start and remain within the DCS during the transition to manual driving. Only if the states are within the DCS is the transition to manual driving classified as safe. We demonstrate the estimation of the DCS for four drivers based on the data collected with real vehicles in highway and city driving. Experiments on transitions to manual driving are also conducted with real vehicles. Results show that the proposed method can be implemented with a real system to classify transitions from automated to manual driving
Driver performance in the presence of adaptive cruise control related failures: Implications for safety analysis and fault tolerance
No full textThis study explored how failures related to an adaptive cruise control (ACC) were handled by drivers and what the effects on safety can be. The experimental study included forty-eight subjects and was performed in a moving base driving simulator equipped with an ACC. Each subject experienced two different failures in separate scenarios. In total, the study included four different failures, i.e., Unwanted acceleration, Complete lack of deceleration, Partial lack of deceleration, and Speed limit violation. The outcome of each failure scenario has been categorized based on whether the driver managed to avoid a collision or not. For the outcomes where collisions were successfully avoided, the situations were analyzed in more detail and classified according to the strategy used by the driver. Besides showing that partial lack of deceleration caused more collisions than complete lack of deceleration (43% compared to 14% of the participants colliding), the results also indicate a preference among drivers to steer and change lane rather than to apply the brakes when faced with acceleration and deceleration failures. A trade off relationship was identified between allowing a failing ACC to stay operational and on the other hand disabling it when an error is detected. Keeping the system operational can cause confusion about the mode of the system but as the results of the study indicate it can also improve the situation by reducing impact speed
A brief paper on improving active safety systems via HMI and dependability analysis
No full textAs new active safety systems are introduced in vehicles it is important to assure correct functionality also in the presence of faults. If faults are not considered in the design of such systems, the claimed safety benefits may be compromised. It is also important to take a holistic view in the design due to the close interaction between the driver and the control systems. There exist techniques for minimizing and tolerating faults in control systems and the design of interfaces for HMI (Human-Machine-Interaction) can be aided by models of human behaviour. This paper outlines on-going research on this close interaction between the driver and the vehicle with the aim of improving safety. The overall methods for attaining improved safety is to apply techniques for minimizing and tolerating faults in active safety systems based on recommendations from analysis of dependable systems and driver behaviour
Validation & Certification of Safety-Critical Embedded Systems - The DECOS Test Bench
No full text
On the Effects of Soft Errors in Embedded Control Systems
No full textThis thesis investigates techniques for making closed loop control systems fault-tolerant and robust with respect to soft errors occurring in the computer hardware. Soft errors are caused by transient faults that alter the binary values stored in latches, flip-flops and other state elements without causing any permanent damage to the hardware. Soft errors caused by ionizing particles such as high energy neutrons are expected to become a dominating source of hardware failures in future digital circuits. Software implemented techniques for detecting and tolerating soft errors for closed loop control systems are proposed and evaluated. These software techniques are designed to serve as a complement to hardware implemented error detection and correction mechanisms that are present in most computer systems. The objective is to provide a software layer of fault-tolerance mechanisms that can detect, mask or recover from soft errors that escape the hardware mechanisms. Fault injection experiments with control systems for both a four-stroke combustion engine and a jet engine show that a majority of the soft errors (single bit-flips) in CPU-registers and memory have no or minor impact on the behavior of the engines. However, the experiments also show that a small but significant number of the errors result in critical engine failures. These critical failures are predominantly caused by soft errors affecting the state variables of the control algorithm. We present the design and validation of two error detection and recovery techniques called Best Effort Recovery and the Robust Integrator. These techniques are designed to protect the controller state and are experimentally validated by fault injection experiments. The Best Effort Recovery technique performs a rollback recovery if the state variables or the control output are outside defined value bounds. The Robust Integrator is constructed as a generic component in a tool for model-based design and can thus be used for robust implementation of a wide range of control algorithms. To validate these techniques, we have developed a new fault injection tool called GOOFI (Generic Object-Oriented Fault Injection). The tool has been designed to be easily adaptable to different target systems and simple to extend with new fault injection techniques and fault models
On the Effects of Soft Errors in Embedded Control Systems
No full textThis thesis investigates techniques for making closed loop control systems fault-tolerant and robust with respect to soft errors occurring in the computer hardware. Soft errors are caused by transient faults that alter the binary values stored in latches, flip-flops and other state elements without causing any permanent damage to the hardware. Soft errors caused by ionizing particles such as high energy neutrons are expected to become a dominating source of hardware failures in future digital circuits. Software implemented techniques for detecting and tolerating soft errors for closed loop control systems are proposed and evaluated. These software techniques are designed to serve as a complement to hardware implemented error detection and correction mechanisms that are present in most computer systems. The objective is to provide a software layer of fault-tolerance mechanisms that can detect, mask or recover from soft errors that escape the hardware mechanisms. Fault injection experiments with control systems for both a four-stroke combustion engine and a jet engine show that a majority of the soft errors (single bit-flips) in CPU-registers and memory have no or minor impact on the behavior of the engines. However, the experiments also show that a small but significant number of the errors result in critical engine failures. These critical failures are predominantly caused by soft errors affecting the state variables of the control algorithm. We present the design and validation of two error detection and recovery techniques called Best Effort Recovery and the Robust Integrator. These techniques are designed to protect the controller state and are experimentally validated by fault injection experiments. The Best Effort Recovery technique performs a rollback recovery if the state variables or the control output are outside defined value bounds. The Robust Integrator is constructed as a generic component in a tool for model-based design and can thus be used for robust implementation of a wide range of control algorithms. To validate these techniques, we have developed a new fault injection tool called GOOFI (Generic Object-Oriented Fault Injection). The tool has been designed to be easily adaptable to different target systems and simple to extend with new fault injection techniques and fault models
- …
