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Found 16 papers, 8 papers with code
A Survey on Multimodal Large Language Models for Autonomous Driving
We first introduce the background of Multimodal Large Language Models (MLLMs), the multimodal models development using LLMs, and the history of autonomous driving.
Radar Enlighten the Dark: Enhancing Low-Visibility Perception for Automated Vehicles with Camera-Radar Fusion
Sensor fusion is a crucial augmentation technique for improving the accuracy and reliability of perception systems for automated vehicles under diverse driving conditions.
ViT-DD: Multi-Task Vision Transformer for Semi-Supervised Driver Distraction Detection
Ensuring traffic safety and mitigating accidents in modern driving is of paramount importance, and computer vision technologies have the potential to significantly contribute to this goal.
DeepFood: Deep Learning-Based Food Image Recognition for Computer-Aided Dietary Assessment
We applied our proposed approach to two real-world food image data sets (UEC-256 and Food-101) and achieved impressive results.
MACP: Efficient Model Adaptation for Cooperative Perception
We approach this objective by identifying the key challenges of shifting from single-agent to cooperative settings, adapting the model by freezing most of its parameters and adding a few lightweight modules.
M$^2$DAR: Multi-View Multi-Scale Driver Action Recognition with Vision Transformer
Ensuring traffic safety and preventing accidents is a critical goal in daily driving, where the advancement of computer vision technologies can be leveraged to achieve this goal.
Mitigating Transformer Overconfidence via Lipschitz Regularization
Though Transformers have achieved promising results in many computer vision tasks, they tend to be over-confident in predictions, as the standard Dot Product Self-Attention (DPSA) can barely preserve distance for the unbounded input domain.
LaMPilot: An Open Benchmark Dataset for Autonomous Driving with Language Model Programs
Autonomous driving (AD) has made significant strides in recent years.
An End-to-End Visual-Audio Attention Network for Emotion Recognition in User-Generated Videos
Emotion recognition in user-generated videos plays an important role in human-centered computing.
Ranked #4 on
Video Emotion Recognition
on Ekman6
Peer-to-Peer Federated Continual Learning for Naturalistic Driving Action Recognition
Naturalistic driving action recognition (NDAR) has proven to be an effective method for detecting driver distraction and reducing the risk of traffic accidents.
CEMFormer: Learning to Predict Driver Intentions from In-Cabin and External Cameras via Spatial-Temporal Transformers
Driver intention prediction seeks to anticipate drivers' actions by analyzing their behaviors with respect to surrounding traffic environments.
Drive as You Speak: Enabling Human-Like Interaction with Large Language Models in Autonomous Vehicles
The future of autonomous vehicles lies in the convergence of human-centric design and advanced AI capabilities.
Receive, Reason, and React: Drive as You Say with Large Language Models in Autonomous Vehicles
The fusion of human-centric design and artificial intelligence (AI) capabilities has opened up new possibilities for next-generation autonomous vehicles that go beyond transportation.
Large Language Models for Autonomous Driving: Real-World Experiments
Autonomous driving systems are increasingly popular in today's technological landscape, where vehicles with partial automation have already been widely available on the market, and the full automation era with "driverless" capabilities is near the horizon.
Spurious Correlations in Machine Learning: A Survey
Machine learning systems are known to be sensitive to spurious correlations between biased features of the inputs (e. g., background, texture, and secondary objects) and the corresponding labels.
Quantifying Uncertainty in Motion Prediction with Variational Bayesian Mixture
In this paper, we propose the Sequential Neural Variational Agent (SeNeVA), a generative model that describes the distribution of future trajectories for a single moving object.
