Search Results for author: Nemanja Djuric

Found 24 papers, 5 papers with code

Growing Adaptive Multi-hyperplane Machines

1 code implementation ICML 2020 Nemanja Djuric, Zhuang Wang, Slobodan Vucetic

Adaptive Multi-hyperplane Machine (AMM) is an online algorithm for learning Multi-hyperplane Machine (MM), a classification model which allows multiple hyperplanes per class.

Detection of Active Emergency Vehicles using Per-Frame CNNs and Output Smoothing

no code implementations28 Dec 2022 Meng Fan, Craig Bidstrup, Zhaoen Su, Jason Owens, Gary Yang, Nemanja Djuric

While inferring common actor states (such as position or velocity) is an important and well-explored task of the perception system aboard a self-driving vehicle (SDV), it may not always provide sufficient information to the SDV.

Data Augmentation Position

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

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

Multi-Modal Trajectory Prediction of NBA Players

no code implementations18 Aug 2020 Sandro Hauri, Nemanja Djuric, Vladan Radosavljevic, Slobodan Vucetic

National Basketball Association (NBA) players are highly motivated and skilled experts that solve complex decision making problems at every time point during a game.

Decision Making 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

Proceedings of the 2017 AdKDD & TargetAd Workshop

no code implementations11 Jul 2017 Abraham Bagherjeiran, Nemanja Djuric, Mihajlo Grbovic, Kuang-Chih Lee, Kun Liu, Vladan Radosavljevic, Suju Rajan

Proceedings of the 2017 AdKDD and TargetAd Workshop held in conjunction with the 23rd ACM SIGKDD Conference on Knowledge Discovery and Data Mining Halifax, Nova Scotia, Canada.

Scalable Semantic Matching of Queries to Ads in Sponsored Search Advertising

no code implementations7 Jul 2016 Mihajlo Grbovic, Nemanja Djuric, Vladan Radosavljevic, Fabrizio Silvestri, Ricardo Baeza-Yates, Andrew Feng, Erik Ordentlich, Lee Yang, Gavin Owens

For this reason search engines often provide a service of advanced matching, which automatically finds additional relevant queries for advertisers to bid on.

Non-linear Label Ranking for Large-scale Prediction of Long-Term User Interests

no code implementations29 Jun 2016 Nemanja Djuric, Mihajlo Grbovic, Vladan Radosavljevic, Narayan Bhamidipati, Slobodan Vucetic

We consider the problem of personalization of online services from the viewpoint of ad targeting, where we seek to find the best ad categories to be shown to each user, resulting in improved user experience and increased advertisers' revenue.


Hierarchical Neural Language Models for Joint Representation of Streaming Documents and their Content

no code implementations28 Jun 2016 Nemanja Djuric, Hao Wu, Vladan Radosavljevic, Mihajlo Grbovic, Narayan Bhamidipati

In particular, we exploit the context of documents in streams and use one of the language models to model the document sequences, and the other to model word sequences within them.

Network-Efficient Distributed Word2vec Training System for Large Vocabularies

no code implementations27 Jun 2016 Erik Ordentlich, Lee Yang, Andy Feng, Peter Cnudde, Mihajlo Grbovic, Nemanja Djuric, Vladan Radosavljevic, Gavin Owens

Word2vec is a popular family of algorithms for unsupervised training of dense vector representations of words on large text corpuses.

Portrait of an Online Shopper: Understanding and Predicting Consumer Behavior

no code implementations15 Dec 2015 Farshad Kooti, Kristina Lerman, Luca Maria Aiello, Mihajlo Grbovic, Nemanja Djuric, Vladan Radosavljevic

Linking online shopping to income, we find that shoppers from more affluent areas purchase more expensive items and buy them more frequently, resulting in significantly more money spent on online purchases.

Social and Information Networks Computers and Society

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