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Advances in real-time, embedded and distributed systems along with control and communication theory have catalyzed the rapid emergence of cyber-physical systems such as a self-driving car. The importance of fault-tolerance support on a cyber-physical system (CPS) has been greatly emphasized by recent research due to the nature of CPS that senses its surroundings, processes sensor data, and reacts using its actuators. In order to tackle this challenge, we proposed SAFER (System-level Architecture for Failure Evasion in Real-time Applications) in our previous work. SAFER is able to tolerate fail-stop processor and/or task failures for distributed embedded real-time systems. One of its limitations, however, is that SAFER is not capable of tolerating a failure of a processor with a dedicated connection to an actuator. This paper provides a method that relaxes this limitation by (1) deploying a small piece of hardware to avoid a dedicated connection between a processor and an actuator, (2) adding a software module that monitors and controls the hardware, and (3) enhancing the failure detection and recovery mechanisms of SAFER to support these changes. The detailed implementation and evaluation of the SAFER extension is on-going work.

In this paper, we present two conceptual frameworks for GPU applications to adjust their task execution times based on total workload. These frameworks enable smart GPU resource management when many applications share GPU resources while the workloads of those applications vary. Application developers can explicitly adjust the number of GPU cores depending on their needs. An implicit adjustment will be supported by a run-time framework, which dynamically allocates the number of cores to tasks based on the total workload. The runtime support of the proposed system can be realized using functions which measure the execution times of the tasks on GPU and change the number of GPU cores. We motivate the necessity of this framework in the context of self-driving technologies, and we believe that our frameworks for GPU programming are useful contributions given the increasing emphasis on parallel heterogeneous computing.

Autonomous driving technologies have been emerging over the past few years, and semi-autonomous driving functionalities have been deployed to vehicles available in the market. Since autonomous driving is realized by the intelligent processing of data from various types of sensors such as LIDAR, radar, camera, etc., the complexity of designing a dependable real-time autonomous driving system is rather high. Although there has been much research on building a reliable real-time system using hardware replication, the resulting systems tend to add significant extra cost due to hardware replication. Therefore, an alternative solution would be helpful in building an autonomous vehicle in a cost-effective way. An autonomous driving system is different from the conventional reliable real-time system because it requires (1) flexible design, (2) adaptive graceful degradation and (3) effective use of different modalities of sensors and actuators. To address these characteristics, we summarize SAFER (System-level Architecture for Failure Evasion in Real-time applications) our previous work on flexible system design. We then present a conceptual framework for autonomous vehicles to provide adaptive graceful degradation and support for using different types of sensors/actuators when a failure happens. We motivate our proposed framework with various scenarios, and we describe how SAFER can be extended to support the proposed conceptual framework.