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1. GrokWalks: A Portable Virtual Reality Platform to Facilitate Studying Driver-Pedestrian Interactions

   https://doi.org/10.1145/3641308.3685013  

   Dey, Debargha; Goedicke, David; Yang, Chishang; Sirkin, David; Currano, Rebecca; Ju, Wendy (September 2024, ACM)

   Driving simulators are vital for human-centered automotive research, offering safe, replicable environments for studying human interaction with transportation technology interfaces and behaviors. However, traditional driving simulators are not well-suited to studying traffic interactions with various degrees of freedom in a way that allows for the capture of nuances in implicit and explicit interactions, e.g. gestures, body language, and movement. We developed a multi-participant virtual reality (VR) driving simulation platform to study these interactions. This portable system supports cross-cultural experiments by modeling diverse scenarios, generating analyzable data, and capturing human behaviors in traffic. Our interactive demo allows participants to experience roles as drivers or pedestrians in a shared virtual environment, with the goal of providing a hands-on experience with this open-source VR simulator and demonstrating its affordability and scalability for traffic interaction studies to researchers and practitioners. 

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2. Environmental variability in aquatic ecosystems: Avenues for future multifactorial experiments

   https://doi.org/10.1002/lol2.10286  

   Gerhard, Miriam; Koussoroplis, Apostolos‐Manuel; Raatz, Michael; Pansch, Christian; Fey, Samuel B.; Vajedsamiei, Jahangir; Calderó‐Pascual, Maria; Cunillera‐Montcusí, David; Juvigny‐Khenafou, Noël P. D.; Polazzo, Francesco; et al (October 2022, Limnology and Oceanography Letters)

   Abstract The relevance of considering environmental variability for understanding and predicting biological responses to environmental changes has resulted in a recent surge in variability‐focused ecological research. However, integration of findings that emerge across studies and identification of remaining knowledge gaps in aquatic ecosystems remain critical. Here, we address these aspects by: (1) summarizing relevant terms of variability research including the components (characteristics) of variability and key interactions when considering multiple environmental factors; (2) identifying conceptual frameworks for understanding the consequences of environmental variability in single and multifactorial scenarios; (3) highlighting challenges for bridging theoretical and experimental studies involving transitioning from simple to more complex scenarios; (4) proposing improved approaches to overcome current mismatches between theoretical predictions and experimental observations; and (5) providing a guide for designing integrated experiments across multiple scales, degrees of control, and complexity in light of their specific strengths and limitations. 

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3. Cross-Modality Graph-based Language and Sensor Data Co-Learning of Human-Mobility Interaction

   https://doi.org/10.1145/3610904  

   Tabatabaie, Mahan; He, Suining; Shin, Kang G. (September 2023, Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies)

   Learning the human--mobility interaction (HMI) on interactive scenes (e.g., how a vehicle turns at an intersection in response to traffic lights and other oncoming vehicles) can enhance the safety, efficiency, and resilience of smart mobility systems (e.g., autonomous vehicles) and many other ubiquitous computing applications. Towards the ubiquitous and understandable HMI learning, this paper considers both spoken language (e.g., human textual annotations) and unspoken language (e.g., visual and sensor-based behavioral mobility information related to the HMI scenes) in terms of information modalities from the real-world HMI scenarios. We aim to extract the important but possibly implicit HMI concepts (as the named entities) from the textual annotations (provided by human annotators) through a novel human language and sensor data co-learning design. To this end, we propose CG-HMI, a novel Cross-modality Graph fusion approach for extracting important Human-Mobility Interaction concepts from co-learning of textual annotations as well as the visual and behavioral sensor data. In order to fuse both unspoken and spoken languages, we have designed a unified representation called the human--mobility interaction graph (HMIG) for each modality related to the HMI scenes, i.e., textual annotations, visual video frames, and behavioral sensor time-series (e.g., from the on-board or smartphone inertial measurement units). The nodes of the HMIG in these modalities correspond to the textual words (tokenized for ease of processing) related to HMI concepts, the detected traffic participant/environment categories, and the vehicle maneuver behavior types determined from the behavioral sensor time-series. To extract the inter- and intra-modality semantic correspondences and interactions in the HMIG, we have designed a novel graph interaction fusion approach with differentiable pooling-based graph attention. The resulting graph embeddings are then processed to identify and retrieve the HMI concepts within the annotations, which can benefit the downstream human-computer interaction and ubiquitous computing applications. We have developed and implemented CG-HMI into a system prototype, and performed extensive studies upon three real-world HMI datasets (two on car driving and the third one on e-scooter riding). We have corroborated the excellent performance (on average 13.11% higher accuracy than the other baselines in terms of precision, recall, and F1 measure) and effectiveness of CG-HMI in recognizing and extracting the important HMI concepts through cross-modality learning. Our CG-HMI studies also provide real-world implications (e.g., road safety and driving behaviors) about the interactions between the drivers and other traffic participants. 

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4. Ab initio methods for polariton chemistry

   https://doi.org/10.1063/5.0167243  

   Foley, Jonathan J.; McTague, Jonathan F.; DePrince, A. Eugene (December 2023, Chemical Physics Reviews)

   Polariton chemistry exploits the strong interaction between quantized excitations in molecules and quantized photon states in optical cavities to affect chemical reactivity. Molecular polaritons have been experimentally realized by the coupling of electronic, vibrational, and rovibrational transitions to photon modes, which has spurred a tremendous theoretical effort to model and explain how polariton formation can influence chemistry. This tutorial review focuses on computational approaches for the electronic strong coupling problem through the combination of familiar techniques from ab initio electronic structure theory and cavity quantum electrodynamics, toward the goal of supplying predictive theories for polariton chemistry. Our aim is to emphasize the relevant theoretical details with enough clarity for newcomers to the field to follow, and to present simple and practical code examples to catalyze further development work. 

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5. Driver-Automated Vehicle Interaction in Mixed Traffic: Types of Interaction and Drivers’ Driving Styles

   https://doi.org/10.1177/00187208221088358  

   Ma, Zheng; Zhang, Yiqi (April 2022, Human Factors: The Journal of the Human Factors and Ergonomics Society)

   Objective This study investigated drivers’ subjective feelings and decision making in mixed traffic by quantifying driver’s driving style and type of interaction. Background Human-driven vehicles (HVs) will share the road with automated vehicles (AVs) in mixed traffic. Previous studies focused on simulating the impacts of AVs on traffic flow, investigating car-following situations, and using simulation analysis lacking experimental tests of human drivers. Method Thirty-six drivers were classified into three driver groups (aggressive, moderate, and defensive drivers) and experienced HV-AV interaction and HV-HV interaction in a supervised web-based experiment. Drivers’ subjective feelings and decision making were collected via questionnaires. Results Results revealed that aggressive and moderate drivers felt significantly more anxious, less comfortable, and were more likely to behave aggressively in HV-AV interaction than in HV-HV interaction. Aggressive drivers were also more likely to take advantage of AVs on the road. In contrast, no such differences were found for defensive drivers indicating they were not significantly influenced by the type of vehicles with which they were interacting. Conclusion Driving style and type of interaction significantly influenced drivers’ subjective feelings and decision making in mixed traffic. This study brought insights into how human drivers perceive and interact with AVs and HVs on the road and how human drivers take advantage of AVs. Application This study provided a foundation for developing guidelines for mixed transportation systems to improve driver safety and user experience. 

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