Convolutional Social Pooling for Vehicle Trajectory Prediction

Deo, Nachiket; Trivedi, Mohan M.

doi:10.1109/cvprw.2018.00196

Cited by 760 publications

(773 citation statements)

References 24 publications

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“…a map). Deo et al [11] propose a convolutional social pooling approach wherein they first predict the maneuver and then the trajectory conditioned on that maneuver. In the self-driving domain, the use of spatial context is of utmost importance and it can be efficiently leveraged from the maps.…”

Section: Related Workmentioning

confidence: 99%

Argoverse: 3D Tracking and Forecasting With Rich Maps

Chang

Ramanan²,

Hays³

et al. 2019

2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

1,144

788

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Figure 1: We introduce datasets for 3D tracking and motion forecasting with rich maps for autonomous driving. Our 3D tracking dataset contains sequences of LiDAR measurements, 360 • RGB video, front-facing stereo (middle-right), and 6-dof localization. All sequences are aligned with maps containing lane center lines (magenta), driveable region (orange), and ground height. Sequences are annotated with 3D cuboid tracks (green). A wider map view is shown in the bottom-right. AbstractWe present Argoverse -two datasets designed to support autonomous vehicle machine learning tasks such as 3D tracking and motion forecasting. Argoverse was collected by a fleet of autonomous vehicles in Pittsburgh and Miami. The Argoverse 3D Tracking dataset includes 360 • images from 7 cameras with overlapping fields of view, 3D point clouds from long range LiDAR, 6-DOF pose, and 3D track annotations. Notably, it is the only modern AV dataset that provides forward-facing stereo imagery. The Argoverse Motion Forecasting dataset includes more than 300,000 5 second tracked scenarios with a particular vehicle identified for trajectory forecasting. Argoverse is the first autonomous vehicle dataset to include "HD maps" with 290 km of mapped lanes with geometric and semantic metadata. All data is released under a Creative Commons license at *Equal contribution www.argoverse.org. In our baseline experiments, we illustrate how detailed map information such as lane direction, driveable area, and ground height improves the accuracy of 3D object tracking and motion forecasting. Our tracking and forecasting experiments represent only an initial exploration of the use of rich maps in robotic perception. We hope that Argoverse will enable the research community to explore these problems in greater depth.

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Section: Related Workmentioning

confidence: 99%

Argoverse: 3D Tracking and Forecasting With Rich Maps

Chang

Ramanan²,

Hays³

et al. 2019

2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

1,144

788

View full text Add to dashboard Cite

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“…[KKK*17] proposed an LSTM‐based probabilistic vehicle trajectory prediction approach which uses an occupancy grid map to characterize the driving environment. Deo and Trivedi [DT18] adopt a convolutional social pooling network to predict vehicle trajectories on highways. The whole network includes an LSTM encoder, convolutional social pooling layers, and a maneuver‐based decoder.…”

Section: Applications In Autonomous Drivingmentioning

confidence: 99%

A Survey on Visual Traffic Simulation: Models, Evaluations, and Applications in Autonomous Driving

Chao

et al. 2019

Computer Graphics Forum

105

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Virtualized traffic via various simulation models and real‐world traffic data are promising approaches to reconstruct detailed traffic flows. A variety of applications can benefit from the virtual traffic, including, but not limited to, video games, virtual reality, traffic engineering and autonomous driving. In this survey, we provide a comprehensive review on the state‐of‐the‐art techniques for traffic simulation and animation. We start with a discussion on three classes of traffic simulation models applied at different levels of detail. Then, we introduce various data‐driven animation techniques, including existing data collection methods, and the validation and evaluation of simulated traffic flows. Next, we discuss how traffic simulations can benefit the training and testing of autonomous vehicles. Finally, we discuss the current states of traffic simulation and animation and suggest future research directions.

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“…b) Convolutional Social Pooling (SocialCNN): In [25], a social tensor is created by learning latent vectors of all cars by an encoder network and projecting them to a grid map in order to learn spatial dependencies. c) Vehicle Behaviour Interaction Networks (VBIN): In [24], instead of summarizing the output vectors as in the Deep Sets approach, the vectors are concatenated, which results in a limitation to a fixed number of cars.…”

Section: Comparative Analysismentioning

confidence: 99%

Dynamic Input for Deep Reinforcement Learning in Autonomous Driving

Huegle

Kalweit

Mirchevska

et al. 2019

2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

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The common pipeline in autonomous driving systems is highly modular and includes a perception component which extracts lists of surrounding objects and passes these lists to a high-level decision component. In this case, leveraging the benefits of deep reinforcement learning for high-level decision making requires special architectures to deal with multiple variable-length sequences of different object types, such as vehicles, lanes or traffic signs. At the same time, the architecture has to be able to cover interactions between traffic participants in order to find the optimal action to be taken. In this work, we propose the novel Deep Scenes architecture, that can learn complex interaction-aware scene representations based on extensions of either 1) Deep Sets or 2) Graph Convolutional Networks. We present the Graph-Q and DeepScene-Q off-policy reinforcement learning algorithms, both outperforming state-ofthe-art methods in evaluations with the publicly available traffic simulator SUMO.

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Convolutional Social Pooling for Vehicle Trajectory Prediction

Cited by 760 publications

References 24 publications

Argoverse: 3D Tracking and Forecasting With Rich Maps

Argoverse: 3D Tracking and Forecasting With Rich Maps

A Survey on Visual Traffic Simulation: Models, Evaluations, and Applications in Autonomous Driving

Dynamic Input for Deep Reinforcement Learning in Autonomous Driving

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