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Found 14 papers, 0 papers with code
LaserNet: An Efficient Probabilistic 3D Object Detector for Autonomous Driving
The efficiency results from processing LiDAR data in the native range view of the sensor, where the input data is naturally compact.
Sensor Fusion for Joint 3D Object Detection and Semantic Segmentation
In this paper, we present an extension to LaserNet, an efficient and state-of-the-art LiDAR based 3D object detector.
3D Point Cloud Processing and Learning for Autonomous Driving
We present a review of 3D point cloud processing and learning for autonomous driving.
SDVTracker: Real-Time Multi-Sensor Association and Tracking for Self-Driving Vehicles
Accurate motion state estimation of Vulnerable Road Users (VRUs), is a critical requirement for autonomous vehicles that navigate in urban environments.
LaserFlow: Efficient and Probabilistic Object Detection and Motion Forecasting
In this work, we present LaserFlow, an efficient method for 3D object detection and motion forecasting from LiDAR.
RV-FuseNet: Range View Based Fusion of Time-Series LiDAR Data for Joint 3D Object Detection and Motion Forecasting
We show that our approach significantly improves motion forecasting performance over the existing state-of-the-art.
MultiXNet: Multiclass Multistage Multimodal Motion Prediction
One of the critical pieces of the self-driving puzzle is understanding the surroundings of a self-driving vehicle (SDV) and predicting how these surroundings will change in the near future.
Multi-View Fusion of Sensor Data for Improved Perception and Prediction in Autonomous Driving
Our model builds on a state-of-the-art Bird's-Eye View (BEV) network that fuses voxelized features from a sequence of historical LiDAR data as well as rasterized high-definition map to perform detection and prediction tasks.
LiRaNet: End-to-End Trajectory Prediction using Spatio-Temporal Radar Fusion
In this paper, we present LiRaNet, a novel end-to-end trajectory prediction method which utilizes radar sensor information along with widely used lidar and high definition (HD) maps.
Temporally-Continuous Probabilistic Prediction using Polynomial Trajectory Parameterization
To address this issue we propose a simple and general representation for temporally continuous probabilistic trajectory prediction that is based on polynomial trajectory parameterization.
Uncertainty-Aware Vehicle Orientation Estimation for Joint Detection-Prediction Models
Object detection is a critical component of a self-driving system, tasked with inferring the current states of the surrounding traffic actors.
Investigating the Effect of Sensor Modalities in Multi-Sensor Detection-Prediction Models
Detection of surrounding objects and their motion prediction are critical components of a self-driving system.
Convolutions for Spatial Interaction Modeling
In many different fields interactions between objects play a critical role in determining their behavior.
MVFuseNet: Improving End-to-End Object Detection and Motion Forecasting through Multi-View Fusion of LiDAR Data
In this work, we propose \textit{MVFuseNet}, a novel end-to-end method for joint object detection and motion forecasting from a temporal sequence of LiDAR data.
