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Found 14 papers, 4 papers with code
PBP: Path-based Trajectory Prediction for Autonomous Driving
Goal-based prediction models simplify multimodal prediction by first predicting 2D goal locations of agents and then predicting trajectories conditioned on each goal.
Improving Motion Forecasting for Autonomous Driving with the Cycle Consistency Loss
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.
Importance is in your attention: agent importance prediction for autonomous driving
Trajectory prediction is an important task in autonomous driving.
Ellipse Loss for Scene-Compliant Motion Prediction
Motion prediction is a critical part of self-driving technology, responsible for inferring future behavior of traffic actors in autonomous vehicle's surroundings.
Uncertainty-Aware Vehicle Orientation Estimation for Joint Detection-Prediction Models
Object detection is a critical component of a self-driving system, tasked with inferring the current states of the surrounding traffic actors.
Temporally-Continuous Probabilistic Prediction using Polynomial Trajectory Parameterization
To address this issue we propose a simple and general representation for temporally continuous probabilistic trajectory prediction that is based on polynomial trajectory parameterization.
MultiXNet: Multiclass Multistage Multimodal Motion Prediction
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.
Improving Movement Predictions of Traffic Actors in Bird's-Eye View Models using GANs and Differentiable Trajectory Rasterization
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.
Deep Kinematic Models for Kinematically Feasible Vehicle Trajectory Predictions
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.
Predicting Motion of Vulnerable Road Users using High-Definition Maps and Efficient ConvNets
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.
Multimodal Trajectory Predictions for Autonomous Driving using Deep Convolutional Networks
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.
Uncertainty-aware Short-term Motion Prediction of Traffic Actors for Autonomous Driving
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.
MLtuner: System Support for Automatic Machine Learning Tuning
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.
More Effective Distributed ML via a Stale Synchronous Parallel Parameter Server
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.
