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Nemanja Djuric

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11 papers

2 author rows

ICRA Conference 2021 Conference Paper

Ellipse Loss for Scene-Compliant Motion Prediction

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. In order to ensure safe and efficient operations, prediction models need to output accurate trajectories that obey the map constraints. In this paper, we address this task and propose a novel ellipse loss that allows the models to better reason about scene compliance and predict more realistic trajectories. Ellipse loss penalizes off-road predictions directly in a supervised manner, by projecting the output trajectories into the top-down map frame using a differentiable trajectory rasterizer module. Moreover, it takes into account actor dimensions and orientation, providing more direct training signals to the model. We applied ellipse loss to a recently proposed state-of-the-art joint detection-prediction model to showcase its benefits. Evaluation on large-scale autonomous driving data strongly indicates that the method allows for more accurate and more realistic trajectory predictions.

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IROS Conference 2021 Conference Paper

Temporally-Continuous Probabilistic Prediction using Polynomial Trajectory Parameterization

Zhaoen Su
Chao Wang
Henggang Cui
Nemanja Djuric
Carlos Vallespi-Gonzalez
David Bradley

A commonly-used representation for motion prediction of actors is a sequence of waypoints (comprising positions and orientations) for each actor at discrete future time-points. While regressing waypoints is simple and flexible, it can exhibit unrealistic higher-order derivatives (such as acceleration) and approximation errors at intermediate time steps. To address this issue we propose a general representation for temporally-continuous probabilistic trajectory prediction that regresses polynomial parameterization coefficients. We evaluate the proposed representation on supervised trajectory prediction tasks using two large self-driving data sets. The results show realistic higher-order derivatives and better accuracy at interpolated time-points, as well as the benefits of the inferred noise distributions over the trajectories. Extensive experimental studies based on existing state-of-the-art models demonstrate the effectiveness of the proposed approach relative to other representations in predicting the future motions of vehicle, bicyclist, and pedestrian traffic actors.

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ICRA Conference 2020 Conference Paper

Deep Kinematic Models for Kinematically Feasible Vehicle Trajectory Predictions

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. To unlock this potential one of the critical aspects of the autonomous technology is understanding and predicting future movement of vehicles surrounding the SDV. This work presents a deep-learning- based method for kinematically feasible motion prediction of such traffic actors. Previous work did not explicitly encode vehicle kinematics and instead relied on the models to learn the constraints directly from the data, potentially resulting in kinematically infeasible, suboptimal trajectory predictions. To address this issue we propose a method that seamlessly combines ideas from the AI with physically grounded vehicle motion models. In this way we employ best of the both worlds, coupling powerful learning models with strong feasibility guarantees for their outputs. The proposed approach is general, being applicable to any type of learning method. Extensive experiments using deep convnets on real-world data strongly indicate its benefits, outperforming the existing state-of-the-art.

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ICML Conference 2020 Conference Paper

Growing Adaptive Multi-hyperplane Machines

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. AMM is based on Stochastic Gradient Descent (SGD), with training time comparable to linear Support Vector Machine (SVM) and significantly higher accuracy. On the other hand, empirical results indicate there is a large accuracy gap between AMM and non-linear SVMs. In this paper we show that this performance gap is not due to limited representability of the MM model, as it can represent arbitrary concepts. We set to explain the connection between the AMM and Learning Vector Quantization (LVQ) algorithms, and introduce a novel Growing AMM (GAMM) classifier motivated by Growing LVQ, that imputes duplicate hyperplanes into the MM model during SGD training. We provide theoretical results showing that GAMM has favorable convergence properties, and analyze the generalization bound of the MM models. Experiments indicate that GAMM achieves significantly improved accuracy on non-linear problems, with only slightly slower training compared to AMM. On some tasks GAMM comes close to non-linear SVM, and outperforms other popular classifiers such as Neural Networks and Random Forests.

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ICRA Conference 2019 Conference Paper

Multimodal Trajectory Predictions for Autonomous Driving using Deep Convolutional Networks

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. Self-driving vehicles (SDVs) are expected to prevent road accidents and save millions of lives while improving the livelihood and life quality of many more. However, despite large interest and a number of industry players working in the autonomous domain, there still remains more to be done in order to develop a system capable of operating at a level comparable to best human drivers. One reason for this is high uncertainty of traffic behavior and large number of situations that an SDV may encounter on the roads, making it very difficult to create a fully generalizable system. To ensure safe and efficient operations, an autonomous vehicle is required to account for this uncertainty and to anticipate a multitude of possible behaviors of traffic actors in its surrounding. We address this critical problem and present a method to predict multiple possible trajectories of actors while also estimating their probabilities. The method encodes each actor's surrounding context into a raster image, used as input by deep convolutional networks to automatically derive relevant features for the task. Following extensive offline evaluation and comparison to state-of-the-art baselines, the method was successfully tested on SDVs in closed-course tests.

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AIJ Journal 2016 Journal Article

Semi-supervised combination of experts for aerosol optical depth estimation

Nemanja Djuric
Lakesh Kansakar
Slobodan Vucetic

Aerosols are small airborne particles produced by natural and man-made sources. Aerosol Optical Depth (AOD), recognized as one of the most important quantities in understanding and predicting the Earth's climate, is estimated daily on a global scale by several Earth-observing satellite instruments. Each instrument has different coverage and sensitivity to atmospheric and surface conditions, and, as a result, the quality of AOD estimated by different instruments varies across the globe. We present a semi-supervised method for learning how to aggregate estimations from multiple satellite instruments into a more accurate estimate, where labels come from a small number of accurate and expensive ground-based instruments. The method also accounts for the problem of missing experts, an issue inherent to the AOD estimation task. By assuming a context-dependent prior, the model is capable of incorporating additional information and providing estimates even when there are no available experts. Moreover, the proposed method uses a latent variable to partition the data, so that in each partition the expert AOD estimations are aggregated in a different, optimal way. We applied the method to combine global AOD estimations from 5 instruments aboard 4 satellites, and the results indicate it can successfully exploit labeled and unlabeled data to produce accurate aggregated AOD estimations.

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AAAI Conference 2014 Conference Paper

Non-Linear Label Ranking for Large-Scale Prediction of Long-Term User Interests

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. We propose to address this problem as a task of ranking the ad categories depending on a user’s preference, and introduce a novel label ranking approach capable of efficiently learning non-linear, highly accurate models in large-scale settings. Experiments on a real-world advertising data set with more than 3. 2 million users show that the proposed algorithm outperforms the existing solutions in terms of both rank loss and top-K retrieval performance, strongly suggesting the benefit of using the proposed model on large-scale ranking problems.

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JMLR Journal 2013 Journal Article

BudgetedSVM: A Toolbox for Scalable SVM Approximations

Nemanja Djuric
Liang Lan
Slobodan Vucetic
Zhuang Wang

We present BudgetedSVM, an open-source C++ toolbox comprising highly-optimized implementations of recently proposed algorithms for scalable training of Support Vector Machine (SVM) approximators: Adaptive Multi-hyperplane Machines, Low-rank Linearization SVM, and Budgeted Stochastic Gradient Descent. BudgetedSVM trains models with accuracy comparable to LibSVM in time comparable to LibLinear, solving non-linear problems with millions of high-dimensional examples within minutes on a regular computer. We provide command-line and Matlab interfaces to BudgetedSVM, an efficient API for handling large-scale, high- dimensional data sets, as well as detailed documentation to help developers use and further extend the toolbox. [abs] [ pdf ][ bib ] [ code ] &copy JMLR 2013. ( edit, beta )

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IJCAI Conference 2013 Conference Paper

Multi-Prototype Label Ranking with Novel Pairwise-to-Total-Rank Aggregation

Mihajlo Grbovic
Nemanja Djuric
Slobodan Vucetic

We propose a multi-prototype-based algorithm for online learning of soft pairwise-preferences over labels. The algorithm learns soft label preferences via minimization of the proposed soft rank-loss measure, and can learn from total orders as well as from various types of partial orders. The soft pairwise preference algorithm outputs are further aggregated to produce a total label ranking prediction using a novel aggregation algorithm that outperforms existing aggregation solutions. Experiments on synthetic and real-world data demonstrate stateof-the-art performance of the proposed model.

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IJCAI Conference 2013 Conference Paper

Semi-Supervised Learning for Integration of Aerosol Predictions from Multiple Satellite Instruments

Nemanja Djuric
Lakesh Kansakar
Slobodan Vucetic

Aerosol Optical Depth (AOD), recognized as one of the most important quantities in understanding and predicting the Earth’s climate, is estimated daily on a global scale by several Earth-observing satellite instruments. Each instrument has different coverage and sensitivity to atmospheric and surface conditions, and, as a result, the quality of AOD estimated by different instruments varies across the globe. We present a method for learning how to aggregate AOD estimations from multiple satellite instruments into a more accurate estimation. The proposed method is semi-supervised, as it is able to learn from a small number of labeled data, where labels come from a few accurate and expensive ground-based instruments, and a large number of unlabeled data. The method uses a latent variable to partition the data, so that in each partition the expert AOD estimations are aggregated in a different, optimal way. We applied the method to combine AOD estimations from 5 instruments aboard 4 satellites, and the results indicate that it can successfully exploit labeled and unlabeled data to produce accurate aggregated AOD estimations.

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AAAI Conference 2012 Conference Paper

Convex Kernelized Sorting

Nemanja Djuric
Mihajlo Grbovic
Slobodan Vucetic

Kernelized sorting is a method for aligning objects across two domains by considering within-domain similarity, without a need to specify a cross-domain similarity measure. In this paper we present the Convex Kernelized Sorting method where, unlike in the previous approaches, the cross-domain object matching is formulated as a convex optimization problem, leading to simpler optimization and global optimum solution. Our method outputs soft alignments between objects, which can be used to rank the best matches for each object, or to visualize the object matching and verify the correct choice of the kernel. It also allows for computing hard one-to-one alignments by solving the resulting Linear Assignment Problem. Experiments on a number of cross-domain matching tasks show the strength of the proposed method, which consistently achieves higher accuracy than the existing methods.

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