Search Results for author: Carlos Vallespi-Gonzalez

Found 14 papers, 0 papers with code

MVFuseNet: Improving End-to-End Object Detection and Motion Forecasting through Multi-View Fusion of LiDAR Data

no code implementations21 Apr 2021 Ankit Laddha, Shivam Gautam, Stefan Palombo, Shreyash Pandey, Carlos Vallespi-Gonzalez

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.

Motion Forecasting object-detection +1

Uncertainty-Aware Vehicle Orientation Estimation for Joint Detection-Prediction Models

no code implementations5 Nov 2020 Henggang Cui, Fang-Chieh Chou, Jake Charland, Carlos Vallespi-Gonzalez, Nemanja Djuric

Object detection is a critical component of a self-driving system, tasked with inferring the current states of the surrounding traffic actors.

motion prediction object-detection +1

Temporally-Continuous Probabilistic Prediction using Polynomial Trajectory Parameterization

no code implementations1 Nov 2020 Zhaoen Su, Chao Wang, Henggang Cui, Nemanja Djuric, Carlos Vallespi-Gonzalez, David Bradley

To address this issue we propose a simple and general representation for temporally continuous probabilistic trajectory prediction that is based on polynomial trajectory parameterization.

motion prediction Trajectory Prediction

LiRaNet: End-to-End Trajectory Prediction using Spatio-Temporal Radar Fusion

no code implementations2 Oct 2020 Meet Shah, Zhiling Huang, Ankit Laddha, Matthew Langford, Blake Barber, Sidney Zhang, Carlos Vallespi-Gonzalez, Raquel Urtasun

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.

Trajectory Prediction

Multi-View Fusion of Sensor Data for Improved Perception and Prediction in Autonomous Driving

no code implementations27 Aug 2020 Sudeep Fadadu, Shreyash Pandey, Darshan Hegde, Yi Shi, Fang-Chieh Chou, Nemanja Djuric, Carlos Vallespi-Gonzalez

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.

Autonomous Driving object-detection +2

MultiXNet: Multiclass Multistage Multimodal Motion Prediction

no code implementations3 Jun 2020 Nemanja Djuric, Henggang Cui, Zhaoen Su, Shangxuan Wu, Huahua Wang, Fang-Chieh Chou, Luisa San Martin, Song Feng, Rui Hu, Yang Xu, Alyssa Dayan, Sidney Zhang, Brian C. Becker, Gregory P. Meyer, Carlos Vallespi-Gonzalez, Carl K. Wellington

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.

motion prediction

SDVTracker: Real-Time Multi-Sensor Association and Tracking for Self-Driving Vehicles

no code implementations9 Mar 2020 Shivam Gautam, Gregory P. Meyer, Carlos Vallespi-Gonzalez, Brian C. Becker

Accurate motion state estimation of Vulnerable Road Users (VRUs), is a critical requirement for autonomous vehicles that navigate in urban environments.

Association Autonomous Vehicles +2

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