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In the complex traffic environments, understanding how a focal vehicle interacts (e.g., maneuvers) with various traffic elements (e.g., other vehicles, pedestrians, and road infrastructures), i.e., vehicle-to-X interactions (VXIs), is essential for developing the advanced driving support and intelligent vehicles. To derive the VXI scene understanding, reasoning, and decision support (e.g., suggesting cautious move in response of a pedestrian crossing the street), this work takes into account the recent advances of multi-modality large language models (MLLMs). We develop VXI-SUR, a novel VXI Scene Understanding and Reasoning system based on vision-language modeling. VXI-SUR takes in the visual VXI scene, and generates the structured textual responses that interpret the VXI scene and suggests an appropriate decision (e.g., braking, slowing down). We have designed within VXI-SUR a VXI memory mechanism with both scene and knowledge augmentation mechanisms, and enabled scene-knowledge co-learning to capture complex correspondences across scenes and decisions. We have performed extensive and comprehensive evaluations of VXI-SUR based on an open-source dataset with ∼17k VXI scenes. We have conducted extensive experimentation studies upon VXI-SUR, and corroborated VXI awareness, description preciseness, semantic matching, and quality in understanding and reasoning the complex VXI scenes.
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Cross-Modality Graph-based Language and Sensor Data Co-Learning of Human-Mobility Interaction
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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- Award ID(s):
- 2239897
- PAR ID:
- 10500771
- Publisher / Repository:
- ACM
- Date Published:
- Journal Name:
- Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies
- Volume:
- 7
- Issue:
- 3
- ISSN:
- 2474-9567
- Page Range / eLocation ID:
- 1 to 25
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1145/3610904
