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Found 9 papers, 4 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.
Towards Generalizable and Interpretable Motion Prediction: A Deep Variational Bayes Approach
For interpretability, the model achieves target-driven motion prediction by estimating the spatial distribution of long-term destinations with a variational mixture of Gaussians.
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.
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.
Generalizability Analysis of Graph-based Trajectory Predictor with Vectorized Representation
Results show significant performance degradation due to domain shift, and feature attribution provides insights to identify potential causes of these problems.
An Automated Machine Learning (AutoML) Method for Driving Distraction Detection Based on Lane-Keeping Performance
An AutoML method based on XGBoost, termed AutoGBM, is built as the classifier for prediction and feature ranking.
