6 papers
Adaptive Driving Style for SAE Level-2 Driving Automation: Minimizing Preference Mismatch
Kumar Akash, Zhaobo Zheng, Teruhisa Misu +4
Driving style is a key factor in the comfort and acceptance of automated vehicle (AV) features. In SAE Level-2 automation, where the driver must supervise the system and remain rea…
Too Many Specialists: Emergent Inefficiencies and Bottlenecks for Multi-agent Ad-hoc Collaboration
Benjamin Panny, Shashank Mehrotra, Zahra Zahedi +2
Computational models of collaboration without prior coordination often overlook how heterogeneous agent traits and complex task structures jointly produce systemic bottlenecks, ine…
GENNAV: Polygon Mask Generation for Generalized Referring Navigable Regions
Kei Katsumata, Yui Iioka, Naoki Hosomi +3
We focus on the task of identifying the location of target regions from a natural language instruction and a front camera image captured by a mobility. This task is challenging bec…
Self-Supervised Learning-Based Multimodal Prediction on Prosocial Behavior Intentions
Abinay Reddy Naini, Zhaobo K. Zheng, Teruhisa Misu +1
Human state detection and behavior prediction have seen significant advancements with the rise of machine learning and multimodal sensing technologies. However, predicting prosocia…
Toward Informed AV Decision-Making: Computational Model of Well-being and Trust in Mobility
Zahra Zahedi, Shashank Mehrotra, Teruhisa Misu +1
For future human-autonomous vehicle (AV) interactions to be effective and smooth, human-aware systems that analyze and align human needs with automation decisions are essential. Ac…
Optimal Driver Warning Generation in Dynamic Driving Environment
Chenran Li, Aolin Xu, Enna Sachdeva +2
The driver warning system that alerts the human driver about potential risks during driving is a key feature of an advanced driver assistance system. Existing driver warning techno…