Pedestrian–automated vehicle (AV) encounters in shared spaces often involve hesitation and ambiguity. Vehicle-mounted external human–machine interfaces (eHMIs) can help, but obscured or poorly timed communications create significant challenges. To address this, we present a mobile smart pole interaction unit (SPIU) with integrated cameras and LED displays, designed as a pedestrian-side system to deliver explicit cues (“WALK,” “STOP”). An in-the-wild evaluation of the SPIU (N = 21) using a four-factor analysis (CarBehavior, Mobility, eHMI, SPIU) showed that the SPIU improved understandability, trust, and perceived safety, and reduced workload compared with the baseline, with a combination (eHMI+SPIU) yielding the strongest results. Beyond these quantitative benefits, participants appreciated the mobility of the SPIU for its “clear” and “easy to decide” mediation. This work contributes to (1) a design and deployment framework for a mobile SPIU and (2) an in-the-wild evaluation protocol for pedestrian–AV interactions in nonsignalized spaces. Our work sparks discussions on real world evaluations involving detailed vehicle kinematics and accessible multimodality (e.g., audio), focusing on the role of personal robots as user-side eHMIs.

Reservoir Computing (RC) is a time-efficient computational paradigm derived from Recurrent Neural Networks (RNNs). The Simple Cycle Reservoir (SCR) is an RC model that stands out for its minimalistic design, offering extremely low construction complexity and proven capability of universally approximating time-invariant causal fading memory filters, even in the linear dynamics regime. This paper introduces Multiple Simple Cycle Reservoirs (MSCRs), a multi-reservoir framework that extends Echo State Networks (ESNs) by replacing a single large reservoir with multiple interconnected SCRs. We demonstrate that MSCRs optimized with Particle Swarm Optimization (MSCR-PSO) outperform existing multi-reservoir models, achieving competitive predictive performance with a lower-dimensional state space. By modeling interconnections as a weighted Directed Acyclic Graph (DAG), our approach enables flexible, task-specific network topology adaptation. Numerical simulations on three benchmark time-series prediction tasks confirm these advantages over rival algorithms. These findings highlight the potential of MSCR-PSO as a promising framework for optimizing multi-reservoir systems, providing a foundation for further advancements and applications of interconnected SCRs for developing efficient AI devices.

Online classification of multivariate time series is crucial across diverse domains, from healthcare monitoring to industrial systems. Reservoir computing (RC), which employs a fixed, randomly initialized recurrent neural network to encode temporal data sequentially, has emerged as a promising approach due to its efficient training and ability to capture complex temporal patterns. However, standard RC implementations rely on regression-based readouts that produce temporally unstable outputs and limit interpretability. We introduce Gaussian Reservoir State Analysis (GRSA), a novel approach that combines reservoirs’ temporal processing capabilities with distribution-based classification. GRSA models reservoir responses to sequences in each class using multivariate Gaussian distribution and performs classification based on statistical distances. Through comprehensive evaluation using the UEA Multivariate Time Series Classification Archive, we demonstrate that GRSA significantly outperforms the regression-based RC while providing more stable and interpretable outputs. The method enables continuous classification by generating predictions at each time step and maintains robust performance even with shortened test sequences, making it particularly suitable for early classification tasks. GRSA’s architectural simplicity allows for various extensions and adaptations across different application domains.

As autonomous vehicles (AVs) enter pedestrian-centric environments, existing vehicle-mounted external human–machine interfaces (eHMIs) often fall short in shared spaces due to line-of-sight limitations, inconsistent signaling, and increased decision latency on pedestrians. To address these challenges, we introduce the Smart Pole Interaction Unit (SPIU), an infrastructure-based eHMI that decouples intent signaling from vehicles and provides context-aware, elevated visual cues. We evaluate SPIU using immersive VR-AWSIM simulations in four high-risk urban scenarios: four-way intersections, autonomous mixed traffic, blindspots, and nighttime crosswalks. The experiment was developed in Japan and replicated in Norway, where forty participants engaged in 32 trials each under both SPIU-present and SPIU-absent conditions. Behavioral (response time) and subjective (acceptance scale) data were collected. Results show that SPIU significantly improves pedestrian decision-making, with reductions ranging from 40% to over 80% depending on scenario and cultural context, particularly in complex or low-visibility scenarios. Cross-cultural analyses highlight SPIU’s adaptability across differing urban and social contexts. We release our open-source Smartpole-VR-AWSIM framework to support reproducibility and global advancement of infrastructure-based eHMI research through reproducible and immersive behavioral studies.

In Reservoir Computing methods, Multiple Reservoir Echo State Networks (MRESNs) with multiple reservoir encoders can have better computational abilities than the standard single Echo State Network on many time-series processing tasks. However, several hand-crafted network topologies have been widely adopted for building most existing MRESN-based models, which limits the potential of their performance of an MRESN on a given temporal processing task. In this work, we propose a new method to improve the computational ability of an MRESN based on the perspective of the neural architecture search. To accomplish this goal, we use a binary-encoding method to represent the reservoir network topology of an MRESN and develop a Genetic Algorithm (GA)-based approach to seek a desirable network topology. Experimental results show that MRESNs with the corresponding optimal network topologies searched by our proposed approach have better computational abilities than the baseline MRESN models on four benchmark time-series processing datasets.