Fault tolerance is a critical aspect of modern computing systems, ensuring correct functionality in the presence of faults. This paper presents a comprehensive survey of fault tolerance methods and mitigation techniques in embedded systems, with a focus on both software and hardware faults. Emphasis is placed on real-time embedded systems, considering their resource constraints and the increasing interconnectivity of computing systems in commercial and industrial applications. The survey covers various fault tolerance methods, including hardware, software, and hybrid redundancy. Particular attention is given to software faults, acknowledging their significance as a leading cause of system failures, while also addressing hardware faults and their mitigation. Moreover, the paper explores the challenges posed by soft errors in modern computing systems. The survey concludes by emphasizing the need for continued research and development in fault tolerance methods, specifically in the context of real-time embedded systems, and highlights the potential for extending fault tolerance approaches to diverse computing environments.

The development of autonomous driving cars is a complex activity, which poses challenges about ethics, safety, cybersecurity, and social acceptance. The latter, in particular, poses new problems since passengers are used to manually driven vehicles; hence, they need to move their trust from a person to a computer. To smooth the transition towards autonomous vehicles, a delicate calibration of the driving functions should be performed, making the automation decision closest to the passengers’ expectations. The complexity of this calibration lies in the presence of a person in the loop: different settings of a given algorithm should be evaluated by assessing the human reaction to the vehicle decisions. With this work, we for an objective method to classify the people’s reaction to vehicle decisions. By adopting machine learning techniques, it is possible to analyze the passengers’ emotions while driving with alternative vehicle calibrations. Through the analysis of these emotions, it is possible to obtain an objective metric about the comfort feeling of the passengers. As a result, we developed a proof-of-concept implementation of a simple, yet effective, emotions recognition system. It can be deployed either into real vehicles or simulators, during the driving functions calibration.

This paper describes a novel approach to assess detection mechanisms and their diagnostic coverage, implemented using embedded software, designed to identify random hardware failures affecting digital components. In the literature, many proposals adopting fault injection methods are available, with most of them focusing on transient faults and not considering the functional safety standards requirements. This kind of proposal can benefit developers involved in the automotive market, where strict safety and cost requirements make the adoption of software-only strategies convenient. Hence, we have focused our efforts on compliance with the ISO 26262 automotive functional safety standard. The approach concerns permanent faults affecting microcontrollers and it provides a mapping between the failure mode described in part 11 of the Standard and the chosen fault models. We propose a test bench designed to inject permanent failures into an emulated microcontroller and determine which of them are detected by the embedded software. The main contribution of this paper is a novel fault injection manager integrated with the open-source software GCC, GDB, and QEMU. This test bench manages all the assessment phases, from fault generation to fault injection and the ISA emulation management, up to the classification of the simulation results.

The increasing complexity of the Advanced Driver Assistance Systems (ADAS) is making more difficult to perform the Hazard Analysis and Risk Assessment (HARA). These items require high-performance Electronic Control Units (ECU) with extensive software functionalities. To correctly operate they interact with the driver, environment and other vehicle functions through high-speed in-vehicle networks, as well as a wide range of sensors and actuators. As a result, they implement complex behaviors whose outcome in presence of faults is not trivial to identify and classify as requested by the concept phase included in the most recent functional safety standards. In this paper we present a simulation-based methodology to perform the HARA of a vehicle function by mixing the usual industrial approach, based on the designers' knowledge, with one that makes use of a vehicle-level simulator. The simulation-based approach provides an automatic and systematic method to assess the complex interaction of the item under analysis with other vehicle functions in possibly complex operational situations, thus making the prediction of hazards easier. We choose to demonstrate the approach by applying it to a well-known automotive industry case study: an Advanced Emergency Braking System (AEBS). In this way, it is possible to analyze the effects of the function provided by the item, keeping into account the simulations results and comparing them to similar situations analysis available in literature. Thanks to the obtained simulation-based results, safety engineers can formulate a more objective hypothesis, in particular during the hazard classification subphase.

Traffic accidents due to drivers falling asleep while driving is an important cause of death, and effective techniques for coping with this phenomenon are needed. In this paper we present a simple, yet effective algorithm, based on heart rate variability analysis. Results of the experimental validation are reported: two test sessions were performed, one in domestic environments and one in realistic driving environments by using a dynamic driving simulator. The results confirm the results obtained in the previous work (Groppo et al. , 2020), and the capability of the proposed algorithm in predicting sleep while driving using a physiological signal that can be collected at the wrist of the driver.