Search Results for author:
Found 27 papers, 7 papers with code
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.
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.
LaMPilot: An Open Benchmark Dataset for Autonomous Driving with Language Model Programs
Autonomous driving (AD) has made significant strides in recent years.
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.
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.
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.
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.
Federated Learning for Connected and Automated Vehicles: A Survey of Existing Approaches and Challenges
This survey paper presents a review of the advancements made in the application of FL for CAV (FL4CAV).
Decentralized Federated Learning: A Survey and Perspective
Federated learning (FL) has been gaining attention for its ability to share knowledge while maintaining user data, protecting privacy, increasing learning efficiency, and reducing communication overhead.
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.
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.
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.
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.
A Survey of Federated Learning for Connected and Automated Vehicles
Connected and Automated Vehicles (CAVs) are one of the emerging technologies in the automotive domain that has the potential to alleviate the issues of accidents, traffic congestion, and pollutant emissions, leading to a safe, efficient, and sustainable transportation system.
Metamobility: Connecting Future Mobility with Metaverse
A Metaverse is a perpetual, immersive, and shared digital universe that is linked to but beyond the physical reality, and this emerging technology is attracting enormous attention from different industries.
Federated Transfer-Ordered-Personalized Learning for Driver Monitoring Application
This paper proposes a federated transfer-ordered-personalized learning (FedTOP) framework to address the above problems and test on two real-world datasets with and without system heterogeneity.
Driver Digital Twin for Online Prediction of Personalized Lane Change Behavior
The proposed system is first evaluated on a human-in-the-loop co-simulation platform, and then in a field implementation with three passenger vehicles connected through the 4G/LTE cellular network.
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.
Vision-Cloud Data Fusion for ADAS: A Lane Change Prediction Case Study
To advance the development of visual guidance systems, we introduce a novel vision-cloud data fusion methodology, integrating camera image and Digital Twin information from the cloud to help intelligent vehicles make better decisions.
Digital Twin-Assisted Cooperative Driving at Non-Signalized Intersections
Digital Twin, as an emerging technology related to Cyber-Physical Systems (CPS) and Internet of Things (IoT), has attracted increasing attentions during the past decade.
Motion Estimation of Connected and Automated Vehicles under Communication Delay and Packet Loss of V2X Communications
The emergence of the connected and automated vehicle (CAV) technology enables numerous advanced applications in our transportation system, benefiting our daily travels in terms of safety, mobility, and sustainability.
MOVESTAR: An Open-Source Vehicle Fuel and Emission Model based on USEPA MOVES
In this paper, we introduce an open-source model "MOVESTAR" to calculate the fuel consumption and pollutant emissions of motor vehicles.
Sensor Fusion of Camera and Cloud Digital Twin Information for Intelligent Vehicles
With the rapid development of intelligent vehicles and Advanced Driving Assistance Systems (ADAS), a mixed level of human driver engagements is involved in the transportation system.
Long-Term Prediction of Lane Change Maneuver Through a Multilayer Perceptron
Behavior prediction plays an essential role in both autonomous driving systems and Advanced Driver Assistance Systems (ADAS), since it enhances vehicle's awareness of the imminent hazards in the surrounding environment.
End-to-End Vision-Based Adaptive Cruise Control (ACC) Using Deep Reinforcement Learning
This paper presented a deep reinforcement learning method named Double Deep Q-networks to design an end-to-end vision-based adaptive cruise control (ACC) system.
Lookup Table-Based Consensus Algorithm for Real-Time Longitudinal Motion Control of Connected and Automated Vehicles
Different from previous studies in this field where control gains of the consensus algorithm are pre-determined and fixed, we develop algorithms to build up a lookup table, searching for the ideal control gains with respect to different initial conditions of CAVs in real time.
