Author name cluster

David Bradley

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

4 papers

2 author rows

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.

Details

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.

Details

NeurIPS Conference 2008 Conference Paper

Differentiable Sparse Coding

J. Bagnell
David Bradley

Prior work has shown that features which appear to be biologically plausible as well as empirically useful can be found by sparse coding with a prior such as a laplacian (L1) that promotes sparsity. We show how smoother priors can pre- serve the beneﬁts of these sparse priors while adding stability to the Maximum A-Posteriori (MAP) estimate that makes it more useful for prediction problems. Additionally, we show how to calculate the derivative of the MAP estimate efﬁ- ciently with implicit differentiation. One prior that can be differentiated this way is KL-regularization. We demonstrate its effectiveness on a wide variety of appli- cations, and ﬁnd that online optimization of the parameters of the KL-regularized model can signiﬁcantly improve prediction performance.

PDF Details

NeurIPS Conference 2006 Conference Paper

Boosting Structured Prediction for Imitation Learning

J. Bagnell
Joel Chestnutt
David Bradley
Nathan Ratliff

The Maximum Margin Planning (MMP) (Ratliff et al. , 2006) algorithm solves imitation learning problems by learning linear mappings from features to cost functions in a planning domain. The learned policy is the result of minimum-cost planning using these cost functions. These mappings are chosen so that example policies (or trajectories) given by a teacher appear to be lower cost (with a lossscaled margin) than any other policy for a given planning domain. We provide a novel approach, M M P B O O S T, based on the functional gradient descent view of boosting (Mason et al. , 1999; Friedman, 1999a) that extends MMP by "boosting" in new features. This approach uses simple binary classification or regression to improve performance of MMP imitation learning, and naturally extends to the class of structured maximum margin prediction problems. (Taskar et al. , 2005) Our technique is applied to navigation and planning problems for outdoor mobile robots and robotic legged locomotion.

PDF Details