Search Results for author:
Found 16 papers, 2 papers with code
Beyond Empirical Windowing: An Attention-Based Approach for Trust Prediction in Autonomous Vehicles
Humans' internal states play a key role in human-machine interaction, leading to the rise of human state estimation as a prominent field.
ViCor: Bridging Visual Understanding and Commonsense Reasoning with Large Language Models
We categorize the problem of VCR into visual commonsense understanding (VCU) and visual commonsense inference (VCI).
Identification of Adaptive Driving Style Preference through Implicit Inputs in SAE L2 Vehicles
This work proposes identification of user driving style preference with multimodal signals, so the vehicle could match user preference in a continuous and automatic way.
Effects of Augmented-Reality-Based Assisting Interfaces on Drivers' Object-wise Situational Awareness in Highly Autonomous Vehicles
To understand the effect of highlighting on drivers' SA for objects with different types and locations under various traffic densities, we conducted an in-person experiment with 20 participants on a driving simulator.
Domain Knowledge Driven Pseudo Labels for Interpretable Goal-Conditioned Interactive Trajectory Prediction
To address these issues, we propose a novel approach to avoid KL vanishing and induce an interpretable interactive latent space with pseudo labels.
Driving Anomaly Detection Using Conditional Generative Adversarial Network
The experimental results reveal that recordings annotated with events that are likely to be anomalous, such as avoiding on-road pedestrians and traffic rule violations, have higher anomaly scores than recordings without any event annotation.
Learning Temporally and Semantically Consistent Unpaired Video-to-video Translation Through Pseudo-Supervision From Synthetic Optical Flow
In this work, we propose a novel paradigm that regularizes the spatiotemporal consistency by synthesizing motions in input videos with the generated optical flow instead of estimating them.
Explore the Potential Performance of Vision-and-Language Navigation Model: a Snapshot Ensemble Method
Vision-and-Language Navigation (VLN) is a challenging task in the field of artificial intelligence.
Grounding Human-to-Vehicle Advice for Self-driving Vehicles
We show that taking advice improves the performance of the end-to-end network, while the network cues on a variety of visual features that are provided by advice.
Deep Multi-Task Learning for Anomalous Driving Detection Using CAN Bus Scalar Sensor Data
In this paper, we present a novel multi-task learning based approach that leverages domain-knowledge (maneuver labels) for anomaly detection in driving data.
Unsupervised Data Uncertainty Learning in Visual Retrieval Systems
We introduce an unsupervised formulation to estimate heteroscedastic uncertainty in retrieval systems.
Boosting Standard Classification Architectures Through a Ranking Regularizer
We employ triplet loss as a feature embedding regularizer to boost classification performance.
Exploring Uncertainty in Conditional Multi-Modal Retrieval Systems
We cast visual retrieval as a regression problem by posing triplet loss as a regression loss.
Toward Driving Scene Understanding: A Dataset for Learning Driver Behavior and Causal Reasoning
We present the Honda Research Institute Driving Dataset (HDD), a challenging dataset to enable research on learning driver behavior in real-life environments.
