arXiv subjects

Shunsuke Ito

Publications and source records attributed to Shunsuke Ito.

5 recordsLinked to original sources

arXiv2026

VAST: V2X/Dynamic Map-Aware Autonomous Driving Systems Validation Toolchain

Cooperative autonomous driving in the IoT-to-Edge-to-Cloud continuum requires system-level validation across vehicles, infrastructure sensors, edge-side Dynamic Map services, and in-vehicle autonomous-driving stacks. This paper presents VAST, a V2X/Dynamic Map-aware validation toolchain that connects Scenic, Scenario Simulator v2, AWSIM, Autoware, and SIM-LDM. VAST does not introduce a new search algorithm; instead, it addresses interoperability challenges, including Lanelet2-to-Scenic mapping, ROS 2-based co-simulation through SS2, Dynamic Map object injection into Autoware, and collection of TTC, PET, collision, timeout, and performance measurements. In occluded-intersection scenarios, Lanelet2-compatible constrained sampling increases the edge-case discovery rate from 40.0% to 80.0% and reduces the average time per discovered edge case from 259.7 s to 110.4 s. Under the same generated scenario distribution, Dynamic Map availability reduces the collision rate from 78.0% to 40.0% and increases non-collision outcomes from 22.0% to 60.0%, with statistically significant TTC/PET shifts. A throughput study with 1-16 NPCs shows that sampling remains below 0.1 s, whereas AWSIM/Autoware execution and restart overhead dominate runtime. These results position VAST as a practical validation infrastructure for cooperative autonomous-driving CPSs.

cs.RO↗

arXiv2026

BA-TRACE: Boundary-Aware Trace Reconstruction for Scenario-Based Evaluation of Mixed AUTOSAR Adaptive and ROS 2 Vehicular Embedded Systems

Modern vehicular embedded systems increasingly combine ROS 2-based autonomous-driving stacks with AUTOSAR Adaptive Platform (AUTOSAR AP). Such mixed stacks make scenario-based evaluation hard to interpret because execution paths cross DDS-SOME/IP middleware boundaries between ROS 2 and AUTOSAR AP. Existing simulators and tracing tools execute scenarios or collect platform-local traces but cannot reconstruct cross-domain data flows. This paper presents BA-TRACE, a boundary-aware trace reconstruction framework for scenario-based evaluation of mixed AUTOSAR AP and ROS 2 vehicular embedded systems. BA-TRACE combines ROS 2 trace events, AUTOSAR ara::log events, ARXML-derived structural dependencies, and bridge-level instrumentation to reconstruct an end-to-end execution graph across the DDS-SOME/IP boundary. A case study with an AWSIM/OpenSCENARIO-based object-detection and braking scenario shows that BA-TRACE reconstructs the expected cross-platform path and exposes boundary-specific latency such as point-cloud transfer overhead. The reconstructed topology is used as evidence of traceability, not as proof of behavioral correctness or safety.

cs.SE↗

arXiv2026

A novel sustainable role of compost as a universal protective substitute for fish, chicken, pig, and cattle, and its estimation by structural equation modeling

Natural decomposition of organic matter is essential in food systems, and compost is used worldwide as an organic fermented fertilizer. However, as a feature of the ecosystem, its effects on the animals are poorly understood. Here we show that oral administration of compost and/or its derived thermophilic Bacillaceae, i.e., Caldibacillus hisashii and Weizmannia coagulans, can modulate the prophylactic activities of various industrial animals. The fecal omics analyses in the modulatory process showed an improving trend dependent upon animal species, environmental conditions, and administration. However, structural equation modeling (SEM) estimated the grouping candidates of bacteria and metabolites as standard key components beyond the animal species. In particular, the SEM model implied a strong relationship among partly digesting fecal amino acids, increasing genus Lactobacillus as inhabitant beneficial bacteria and 2-aminoisobutyric acid involved in lantibiotics. These results highlight the potential role of compost for sustainable protective control in agriculture, fishery, and livestock industries.

q-bio.QM↗

arXiv2025

Anti-pathogenic property of thermophile-fermented compost as a feed additive and its in vivo external diagnostic imaging in a fish model

Fermentative recycling of organic matter is important for a sustainable society, but the functionality of fermented products needs to be adequately evaluated. Here, we clarify the antipathogenic properties for fish of a compost-type feed additive fermented by thermophilic Bacillaceae using non-edible marine resources as raw materials. After prior administration of the compost extract to seabream as a fish model for 70 days, the mortality rate after 28 days of exposure to the fish pathogen Edwardsiella reached a maximum of 20%, although the rate was 60% without prior administration. Under such conditions, the serum complement activity of seabream increased, and the recovery time after anesthesia treatment was also fasten. Furthermore, the differences in the degree of smoothness and glossiness of the fish body surface depending on the administration were statistically shown by imaging techniques to evaluate the texture and color tone of field photographs. These results suggest that thermophile-fermented compost is effective as a functional feed additive against fish disease infection, and that such conditions can be estimated by body surface analysis. This study provides a new perspective for the natural symbiosis industry, as well as for the utilization of non-invasive diagnosis to efficiently estimate the quality of its production activities

q-bio.QM↗

arXiv2025

D-AWSIM: Distributed Autonomous Driving Simulator for Dynamic Map Generation Framework

Autonomous driving systems have achieved significant advances, and full autonomy within defined operational design domains near practical deployment. Expanding these domains requires addressing safety assurance under diverse conditions. Information sharing through vehicle-to-vehicle and vehicle-to-infrastructure communication, enabled by a Dynamic Map platform built from vehicle and roadside sensor data, offers a promising solution. Real-world experiments with numerous infrastructure sensors incur high costs and regulatory challenges. Conventional single-host simulators lack the capacity for large-scale urban traffic scenarios. This paper proposes D-AWSIM, a distributed simulator that partitions its workload across multiple machines to support the simulation of extensive sensor deployment and dense traffic environments. A Dynamic Map generation framework on D-AWSIM enables researchers to explore information-sharing strategies without relying on physical testbeds. The evaluation shows that D-AWSIM increases throughput for vehicle count and LiDAR sensor processing substantially compared to a single-machine setup. Integration with Autoware demonstrates applicability for autonomous driving research.

cs.RO↗