34 citations · 53 across the 6 of their papers we have counts for
10 papers
MVFuseNet: Improving End-to-End Object Detection and Motion Forecasting through Multi-View Fusion of LiDAR Data
Ankit Laddha, Shivam Gautam, Stefan Palombo +2
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. Most existing metho…
Investigating the Effect of Sensor Modalities in Multi-Sensor Detection-Prediction Models
Abhishek Mohta, Fang-Chieh Chou, Brian C. Becker +2
Detection of surrounding objects and their motion prediction are critical components of a self-driving system. Recently proposed models that jointly address these tasks rely on a n…
LiRaNet: End-to-End Trajectory Prediction using Spatio-Temporal Radar Fusion
Meet Shah, Zhiling Huang, Ankit Laddha +5
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) map…
Uncertainty-Aware Vehicle Orientation Estimation for Joint Detection-Prediction Models
Henggang Cui, Fang-Chieh Chou, Jake Charland +2
Object detection is a critical component of a self-driving system, tasked with inferring the current states of the surrounding traffic actors. While there exist a number of studies…
RV-FuseNet: Range View Based Fusion of Time-Series LiDAR Data for Joint 3D Object Detection and Motion Forecasting
Ankit Laddha, Shivam Gautam, Gregory P. Meyer +2
Robust real-time detection and motion forecasting of traffic participants is necessary for autonomous vehicles to safely navigate urban environments. In this paper, we present RV-F…
SDVTracker: Real-Time Multi-Sensor Association and Tracking for Self-Driving Vehicles
Shivam Gautam, Gregory P. Meyer, Carlos Vallespi-Gonzalez +1
Accurate motion state estimation of Vulnerable Road Users (VRUs), is a critical requirement for autonomous vehicles that navigate in urban environments. Due to their computational…