Search Results for author: Henggang Cui

Found 14 papers, 4 papers with code

PBP: Path-based Trajectory Prediction for Autonomous Driving

no code implementations7 Sep 2023 Sepideh Afshar, Nachiket Deo, Akshay Bhagat, Titas Chakraborty, Yunming Shao, Balarama Raju Buddharaju, Adwait Deshpande, Henggang Cui

Goal-based prediction models simplify multimodal prediction by first predicting 2D goal locations of agents and then predicting trajectories conditioned on each goal.

Autonomous Driving Inductive Bias +1

Improving Motion Forecasting for Autonomous Driving with the Cycle Consistency Loss

no code implementations31 Oct 2022 Titas Chakraborty, Akshay Bhagat, Henggang Cui

To improve the accuracy of motion forecasting, in this work, we identify a new consistency constraint in this task, that is an agent's future trajectory should be coherent with its history observations and visa versa.

Motion Forecasting

Ellipse Loss for Scene-Compliant Motion Prediction

no code implementations5 Nov 2020 Henggang Cui, Hoda Shajari, Sai Yalamanchi, Nemanja Djuric

Motion prediction is a critical part of self-driving technology, responsible for inferring future behavior of traffic actors in autonomous vehicle's surroundings.

Autonomous Driving motion prediction

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

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 Position

Improving Movement Predictions of Traffic Actors in Bird's-Eye View Models using GANs and Differentiable Trajectory Rasterization

1 code implementation14 Apr 2020 Eason Wang, Henggang Cui, Sai Yalamanchi, Mohana Moorthy, Fang-Chieh Chou, Nemanja Djuric

One of the most critical pieces of the self-driving puzzle is the task of predicting future movement of surrounding traffic actors, which allows the autonomous vehicle to safely and effectively plan its future route in a complex world.

Autonomous Vehicles Motion Forecasting +1

Deep Kinematic Models for Kinematically Feasible Vehicle Trajectory Predictions

no code implementations1 Aug 2019 Henggang Cui, Thi Nguyen, Fang-Chieh Chou, Tsung-Han Lin, Jeff Schneider, David Bradley, Nemanja Djuric

Self-driving vehicles (SDVs) hold great potential for improving traffic safety and are poised to positively affect the quality of life of millions of people.

motion prediction

Predicting Motion of Vulnerable Road Users using High-Definition Maps and Efficient ConvNets

1 code implementation20 Jun 2019 Fang-Chieh Chou, Tsung-Han Lin, Henggang Cui, Vladan Radosavljevic, Thi Nguyen, Tzu-Kuo Huang, Matthew Niedoba, Jeff Schneider, Nemanja Djuric

Following detection and tracking of traffic actors, prediction of their future motion is the next critical component of a self-driving vehicle (SDV) technology, allowing the SDV to operate safely and efficiently in its environment.

motion prediction

Multimodal Trajectory Predictions for Autonomous Driving using Deep Convolutional Networks

2 code implementations18 Sep 2018 Henggang Cui, Vladan Radosavljevic, Fang-Chieh Chou, Tsung-Han Lin, Thi Nguyen, Tzu-Kuo Huang, Jeff Schneider, Nemanja Djuric

Autonomous driving presents one of the largest problems that the robotics and artificial intelligence communities are facing at the moment, both in terms of difficulty and potential societal impact.

Autonomous Driving Motion Planning +1

Uncertainty-aware Short-term Motion Prediction of Traffic Actors for Autonomous Driving

no code implementations17 Aug 2018 Nemanja Djuric, Vladan Radosavljevic, Henggang Cui, Thi Nguyen, Fang-Chieh Chou, Tsung-Han Lin, Nitin Singh, Jeff Schneider

We address one of the crucial aspects necessary for safe and efficient operations of autonomous vehicles, namely predicting future state of traffic actors in the autonomous vehicle's surroundings.

Autonomous Driving motion prediction

MLtuner: System Support for Automatic Machine Learning Tuning

1 code implementation20 Mar 2018 Henggang Cui, Gregory R. Ganger, Phillip B. Gibbons

MLtuner automatically tunes settings for training tunables (such as the learning rate, the momentum, the mini-batch size, and the data staleness bound) that have a significant impact on large-scale machine learning (ML) performance.

BIG-bench Machine Learning General Classification +2

More Effective Distributed ML via a Stale Synchronous Parallel Parameter Server

no code implementations NeurIPS 2013 Qirong Ho, James Cipar, Henggang Cui, Seunghak Lee, Jin Kyu Kim, Phillip B. Gibbons, Garth A. Gibson, Greg Ganger, Eric P. Xing

We propose a parameter server system for distributed ML, which follows a Stale Synchronous Parallel (SSP) model of computation that maximizes the time computational workers spend doing useful work on ML algorithms, while still providing correctness guarantees.

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