no code implementations • 9 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.
no code implementations • 12 Mar 2020 • Gregory P. Meyer, Jake Charland, Shreyash Pandey, Ankit Laddha, Shivam Gautam, Carlos Vallespi-Gonzalez, Carl K. Wellington
In this work, we present LaserFlow, an efficient method for 3D object detection and motion forecasting from LiDAR.
no code implementations • 21 May 2020 • Ankit Laddha, Shivam Gautam, Gregory P. Meyer, Carlos Vallespi-Gonzalez, Carl K. Wellington
We show that our approach significantly improves motion forecasting performance over the existing state-of-the-art.
no code implementations • 31 Jul 2020 • Shubhankar Agarwal, Harshit Sikchi, Cole Gulino, Eric Wilkinson, Shivam Gautam
A popular way to plan trajectories in dynamic urban scenarios for Autonomous Vehicles is to rely on explicitly specified and hand crafted cost functions, coupled with random sampling in the trajectory space to find the minimum cost trajectory.
no code implementations • 21 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.