Search Results for author:
Found 9 papers, 4 papers with code
V2V-LLM: Vehicle-to-Vehicle Cooperative Autonomous Driving with Multi-Modal Large Language Models
Inspired by recent progress using Large Language Models (LLMs) to build autonomous driving systems, we propose a novel problem setting that integrates a Multi-Modal LLM into cooperative autonomous driving, with the proposed Vehicle-to-Vehicle Question-Answering (V2V-QA) dataset and benchmark.
Probabilistic 3D Multi-Object Cooperative Tracking for Autonomous Driving via Differentiable Multi-Sensor Kalman Filter
However, their proposed methods mainly use cooperative detection results as input to a standard single-sensor Kalman Filter-based tracking algorithm.
Collision Avoidance Detour for Multi-Agent Trajectory Forecasting
We present our approach, Collision Avoidance Detour (CAD), which won the 3rd place award in the 2023 Waymo Open Dataset Challenge - Sim Agents, held at the 2023 CVPR Workshop on Autonomous Driving.
Selective Communication for Cooperative Perception in End-to-End Autonomous Driving
The reliability of current autonomous driving systems is often jeopardized in situations when the vehicle's field-of-view is limited by nearby occluding objects.
Probabilistic 3D Multi-Modal, Multi-Object Tracking for Autonomous Driving
Second, we propose to learn a metric that combines the Mahalanobis and feature distances when comparing a track and a new detection in data association.
Probabilistic 3D Multi-Object Tracking for Autonomous Driving
Our method estimates the object states by adopting a Kalman Filter.
Imitation Learning for Human Pose Prediction
Modeling and prediction of human motion dynamics has long been a challenging problem in computer vision, and most existing methods rely on the end-to-end supervised training of various architectures of recurrent neural networks.
Ranked #2 on
Human Pose Forecasting
on Human3.6M
(MAR, walking, 1,000ms metric)
Segmenting the Future
While prior work attempts to predict future video pixels, anticipate activities or forecast future scene semantic segments from segmentation of the preceding frames, methods that predict future semantic segmentation solely from the previous frame RGB data in a single end-to-end trainable model do not exist.
Action-Agnostic Human Pose Forecasting
In this paper, we propose a new action-agnostic method for short- and long-term human pose forecasting.
Ranked #5 on
Human Pose Forecasting
on Human3.6M
(MAR, walking, 1,000ms metric)
