AUTOBEST: a united AUTOSAR-OS and ARINC 653 kernel

Zuepke, Alexander; Bommert, Marc; Lohmann, Daniel

doi:10.1109/rtas.2015.7108435

Cited by 14 publications

(5 citation statements)

References 13 publications

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“…The macrotick defines the preemption granularity. The macrotick is set such that it allows preemption, under the constraint that the overhead due to context switches on each processor should be low, see ( Aichouch et al, 2013 ; Craciunas et al, 2014 ; Zuepke et al, 2015 ) for a discussion.…”

Section: Mapping and Scheduling Strategymentioning

confidence: 99%

Configuring ADAS Platforms for Automotive Applications Using Metaheuristics

et al. 2022

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Modern Advanced Driver-Assistance Systems (ADAS) combine critical real-time and non-critical best-effort tasks and messages onto an integrated multi-core multi-SoC hardware platform. The real-time safety-critical software tasks have complex interdependencies in the form of end-to-end latency chains featuring, e.g., sensing, processing/sensor fusion, and actuating. The underlying real-time operating systems running on top of the multi-core platform use static cyclic scheduling for the software tasks, while the communication backbone is either realized through PCIe or Time-Sensitive Networking (TSN). In this paper, we address the problem of configuring ADAS platforms for automotive applications, which means deciding the mapping of tasks to processing cores and the scheduling of tasks and messages. Time-critical messages are transmitted in a scheduled manner via the timed-gate mechanism described in IEEE 802.1Qbv according to the pre-computed Gate Control List (GCL) schedule. We study the computation of the assignment of tasks to the available platform CPUs/cores, the static schedule tables for the real-time tasks, as well as the GCLs, such that task and message deadlines, as well as end-to-end task chain latencies, are satisfied. This is an intractable combinatorial optimization problem. As the ADAS platforms and applications become increasingly complex, such problems cannot be optimally solved and require problem-specific heuristics or metaheuristics to determine good quality feasible solutions in a reasonable time. We propose two metaheuristic solutions, a Genetic Algorithm (GA) and one based on Simulated Annealing (SA), both creating static schedule tables for tasks by simulating Earliest Deadline First (EDF) dispatching with different task deadlines and offsets. Furthermore, we use a List Scheduling-based heuristic to create the GCLs in platforms featuring a TSN backbone. We evaluate the proposed solution with real-world and synthetic test cases scaled to fit the future requirements of ADAS systems. The results show that our heuristic strategy can find correct solutions that meet the complex timing and dependency constraints at a higher rate than the related work approaches, i.e., the jitter constraints are satisfied in over 6 times more cases, and the task chain constraints are satisfied in 41% more cases on average. Our method scales well with the growing trend of ADAS platforms.

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Section: Mapping and Scheduling Strategymentioning

confidence: 99%

Configuring ADAS Platforms for Automotive Applications Using Metaheuristics

et al. 2022

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“…The macrotick defines the preemption granularity. The macrotick is set such that it allows preemption, under the constraint that the overhead due to context switches, on each processor, should be low, see [11], [34], [35] for a discussion.…”

Section: Edf Simulation For Schedule Synthesismentioning

confidence: 99%

Mapping and Scheduling Automotive Applications on ADAS Platforms using Metaheuristics

McLean

Craciunas

Hansen

et al. 2020

2020 25th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA)

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Modern Advanced Driver-Assistance Systems (ADAS) merge critical and non-critical software functions with complex timing requirements and inter-dependencies onto the same integrated hardware platform. Real-time safetycritical automotive applications feature complex dependency chains between tasks (e.g., performing sensing, processing and actuation) which have to satisfy worst-case end-to-end latency constraints. The resulting scheduling problem requires both the assignment of tasks to the available cores of the platform and the computation static schedule tables for the real-time tasks, such that task deadlines, as well as end-to-end task chain constraints, are satisfied. We propose a heuristic approach based on Simulated Annealing (SA) which creates static schedule tables by simulating Earliest Deadline First (EDF) scheduling parameterized by task offsets and local deadlines decided by SA. We evaluate the proposed solution with real-world and synthetic test cases scaled to fit the future requirements of ADAS systems.

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“…As operating system, we use AUTOBEST [4], which was developed and implemented at the authors' faculty in a research project together with Easycore GmbH in Erlangen, Germany. Application areas are safety-critical automotive use cases following the standard ISO26262 [6], which requires "freedom of interference" between independent software components.…”

Section: A Autobestmentioning

confidence: 99%

“…In this paper, the system's software components are decomposed into isolated containers. The system comprises (1) two synthetic test applications sending and receiving data, (2) sDDS [1], a publish/subscribe middleware based on the Data Distribution Service (DDS) standard [2], (3) the lightweight TCP/IP stack (lwIP) [3], (4) an Ethernet driver for the STM32F4 microcontroller, and (5) AUTOBEST [4], a small partitioning microkernel developed for safety-critical automotive use cases. These software components form a vertically layered system model, where higher level components rely on services of the lower level components.…”

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confidence: 99%

For Safety and Security Reasons: The Cost of Component-Isolation in IoT

Zuepke

Beckmann

Zoor

et al. 2016

Logistics &Amp; Sustainable Transport

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The current development trend of Internet of Things (IoT) aims for a tighter integration of mobile and stationary devices via various networks. This includes communication of vehicles to roadside infrastructure (V2I), as well as intelligent sensors / actors in Logistics and smart home environments. Compared to isolated traditional embedded systems, the exposure to open networks increases the attack surface, and errors in the networking components could compromise the safety and security of the embedded application or the whole network. But often current system architectures for mass-market IoT devices lack the required isolation concepts. Using a partitioning microkernel and enforcing the use of a microcontroller’s memory protection unit (MPU) facilities, we compare different isolation concepts for a publish/subscribe middleware implementing OMG’s Data Distribution Service (DDS) standard and we evaluate our results on an STM32F4 microcontroller. The results of this case study show moderate costs for increased memory usage and additional context switches.

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AUTOBEST: a united AUTOSAR-OS and ARINC 653 kernel

Cited by 14 publications

References 13 publications

Configuring ADAS Platforms for Automotive Applications Using Metaheuristics

Configuring ADAS Platforms for Automotive Applications Using Metaheuristics

Mapping and Scheduling Automotive Applications on ADAS Platforms using Metaheuristics

For Safety and Security Reasons: The Cost of Component-Isolation in IoT

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