The field of autonomous vehicle research is advancing rapidly, necessitating platforms that meet real-time performance, safety, and security requirements for practical deployment. AUTOSAR Adaptive Platform (AUTOSAR AP) is widely adopted in development to meet these criteria; however, licensing constraints and tool implementation challenges limit its use in research. Conversely, Robot Operating System 2 (ROS 2) is predominantly used in research within the autonomous driving domain, leading to a disparity between research and development platforms that hinders swift commercialization. This paper proposes a collaboration framework that enables AUTOSAR AP and ROS 2 to communicate with each other using a Data Distribution Service for Real-Time Systems (DDS). In contrast, AUTOSAR AP uses Scalable service-Oriented Middleware over IP (SOME/IP) for communication. The proposed framework bridges these protocol differences, ensuring seamless interaction between the two platforms. We validate the functionality and performance of our bridge converter through empirical analysis, demonstrating its efficiency in conversion time and ease of integration with ROS 2 tools. Furthermore, the availability of the proposed collaboration framework is improved by generating a configuration file automatically for the proposed bridge converter.

Collaborative work between workers and autonomous systems in the same area is required to improve operation efficiency. However, there exist collision risks caused by coexistence of workers and autonomous systems. The safety functions of the autonomous systems, such as emergency stops, can reduce the risks and but may decrease the operation efficiency. Therefore, we propose a novel safety concept called Symbiotic Safety. The concept improves both safety and operation efficiency by transformation of action plan, e.g., adjustment of action plan or update of safety rule, which reduces frequency of risk occurrence and suppress efficiency loss due to safety functions. In this paper, we explain the symbiotic safety technologies and share results of an evaluation experiment by utilizing our prototype system.

We present a middleware-based protocol that reliably synchronizes large samples consisting of multiple frames efficiently and within application level QoS requirements over a lossy wireless channel. The protocol uses a custom retransmission scheme, exploiting the latency requirements on sample level for frame level scheduling. It can be integrated into the popular DDS middleware. We investigate some technical limits of such a protocol and compare it to existing error protocols in the software stack and in the wireless protocol and combinations thereof. The comparison is based on an Omnet++ simulation using an established wireless channel error model. For evaluation, we take a use case from automated valet parking where infrastructure data provided via a wireless link augments in-vehicle sensor data. The use case respects the related safety requirements. Results show that the application awareness of the presented protocol, significantly improves service availability by transmitting data efficiently in time even under higher frame error rates.

Embedded systems, e.g., self-driving systems and advanced driver-assistance systems (ADAS), require computing platforms with high computing power and low power consumption. Multi-/many-core platforms satisfy these requirements effectively. However, for hard real-time applications, multiple demands on shared resources can impede real-time performance, and memory is one resource that can impair the desired performance significantly. Therefore, it is important that memory access timing be deterministic to facilitate predictability. To realize this, the Logical Execution Time (LET) paradigm is currently attracting attention. This paper proposes a theoretical scheduling method for a model applying the LET paradigm to directed acyclic graph (DAG) nodes for a multi-/many-core platform. The proposed method considers communication timing between nodes and generates a schedule that does not cause communication contentions. In addition, the proposed method attempts to distribute tasks and reduce LET intervals to address increased execution times due to the implementation of the LET paradigm. In the evaluation, we observed that the proposed method improved the schedule length by up to 40%.

Computing platforms for embedded systems are increasingly being transformed into multi/many-core platforms because embedded systems have become extensive, complex, and automated. In the case of an autonomous driving system, various applications are simultaneously running, and low power consumption and large-scale calculation are required. Many-core processors with a multiple instruction, multiple data (MIMD) architecture can meet these requirements. This paper proposes a scheduling algorithm for an automotive driving system expressed in a directed acyclic graph (DAG) and we use Kalray MPPA-256 as the target many-core processor. On the basis of the architecture of Kalray MPPA-256, task processing that requires large-scale calculation and intercore communication is performed while avoiding communication contention by using a proposed grouping computational resource. In addition, we propose a scheduling method for a multi-rate DAG which is a DAG with multiple periods. This method generates a DAG task in a hyperperiod and schedules the DAG with dependency on tasks that have been released closely. The formulas for prioritization and processor selection are proposed for various generated tasks in a hyperperiod. Evaluation results show that the proposed algorithm is superior to existing DAG scheduling algorithms with regard to schedulability and deadline miss ratio.

This paper proposes a directed acyclic graph (DAG) scheduling algorithm for cluster-based many-core architecture. Most of DAG scheduling methods that consider multiple processors and communication delays use a heuristic approach because it is difficult to shorten a schedule length (i.e.,makespan). Unfortunately, existing heuristic algorithms do not consider tasks that require a large number of computational resources. Such tasks typically need to be offloaded to a large-scale computational resource such as a many-core system or graphics processing unit (GPU). Therefore, we propose a DAG scheduling algorithm that uses a cluster-based many-core architecture to offload such computation; here, we use Kalray MPPA-256 as the many-core architecture. Our algorithm divides many-core computational resources for such tasks. Comparing our results with existing algorithms, we succeeded in shortening makespan with many types of DAGs while also considering Amdahl's law. We also compared the deadline miss ratio and execution time of our algorithm to existing algorithms, and show that the proposed algorithm is superior to the existing algorithms for any evaluation metrics.

Driverless autonomous driving systems require cost-effective architecture satisfying design diversity and real-time performance to fulfill the fail-operational requirements that sustain system safety if a failure occurs during automated driving. However, conventional approaches cannot be applied to the systems due to design diversity. A key challenge in establishing a cost-effective multi-mode architecture is how to enhance the real-time capability of the mode switch. In this work, we propose three replication methods for fail-operational autonomous driving systems with design diversity: Input Backup Replication (IBR), Extended Primary Backup Replication (E-PBR), and Extended Leader Follower Replication (E-LFR). These methods enable accelerated recovery processing by utilizing input data and internal state backup in addition to partial hot standby. We implemented an autonomous driving prototype and found that (i) the proposed replication methods can satisfy the performance requirements for fail-operational systems, (ii) they can reduce 53.8 % of the CPU load compared with the hot standby approach in the normal mode, and (iii) the memory consumption ratio caused by the proposed methods is 0.01%. These results demonstrate that our proposed replication methods are feasible for fail-operational autonomous driving systems with design diversity.

This paper proposes an anomaly prediction method based on k-means clustering that assumes embedded devices with memory constraints to predict control system anomalies. With this method, by checking control system behavior, it is possible to predict anomalies. However, continuing clustering is difficult because data accumulate in memory similar to existing k-means clustering method, which is problematic for embedded devices with low memory capacity. Therefore, we also propose k-means clustering to continue clustering for infinite stream data. The proposed k-means clustering method is based on online k-means clustering of sequential processing. The proposed k-means clustering method only stores data required for anomaly prediction and releases other data from memory. Experimental results show that anomalies can be predicted by k-means clustering, and the proposed method can predict anomalies similar to standard k-means clustering while reducing memory consumption. Moreover, the proposed k-means clustering demonstrates better results of anomaly prediction than existing online k-means clustering.

Most automotive control systems have been developed by combining legacy Electronic Control Units (ECUs) with newly developed ECUs. We need to plan the product line to utilize as many legacy ECUs as possible to lower development costs of new vehicles. However, we must develop several new gateway ECUs per new vehicle because gateway ECUs depend on the placement of other ECUs and their combinations within the whole system. The reusable gateway ECUs are needed to lower development cost. However, there is a problem in that the worst case execution time (WCET) of relay processing may be impaired in typical methods that are synchronized with the communication cycle of time-triggered networks. Therefore, we propose a Dynamic Activation Timing Configuration (DATC) for product line development, which can dynamically calculate the activation timing of relay processing from the information on message routing, slot assignment, and time-triggered network. The gateway ECUs with DATC can be dynamically adjusted to new vehicles and improve the WCET of relay processing.

The paper presents a distributed computing environment for embedded control systems with time-triggered and event-triggered distributed processing. We have already presented a time-triggered distributed object model and a time-triggered distributed computing environment for embedded control systems. However, there are many embedded control systems with time-triggered and event-triggered processing. In this paper, we present two kinds of event-triggered distributed object models, a pure event-triggered distributed object model and a data-triggered distributed object model, in addition to the time-triggered distributed object model. We also present a distributed object computing environment based on a time-division scheduling for the mixed architecture with time-triggered and event-triggered distributed processing. The time division scheduling divides an execution cycle into a time-triggered processing segment and a non-time-triggered processing segment. The time-triggered distributed processing is executed in the former segment, and the event-triggered distributed processing is executed in the latter segment. The distributed object computing environment consists of a real-time operating system with the time division scheduling and distributed computing middleware to support the three kinds of distributed object models. We provide a development environment that generates stubs and configuration data to build distributed control systems.

The paper presents a time-triggered distributed object computing environment for embedded control systems such as automotive control systems. The time-triggered architecture is suitable for hard real-time systems. We present a time-triggered distributed object model as an extension to the time-triggered object model that consists of objects periodically updating their own attribute values. The model is suitable for embedded control systems in which the control logics are designed with control block diagrams. Replica objects are used for efficient location transparency in the model and inter-object communications are local method calls, not remote method invocations. We have developed a distributed computing environment based on the time- triggered distributed object model. The middleware maintains the replicas to be consistent with the original objects using periodic state messages. The middleware executes the replication processing with the stubs that bridge the original objects, the middleware, and the replicas. We have also developed a development environment for embedded control application programs based on the time-triggered distributed object model. The stubs are generated from the interface definitions written in CORBA-compliant IDL.