Scalability in Perception for Autonomous Driving: Waymo Open Dataset

Sun, Pei; Kretzschmar, Henrik; Dotiwalla, Xerxes; Chouard, Aurelien; Patnaik, Vijaysai; Tsui, Paul; Guo, James C. Y.; Zhou, Yin; Chai, Yuning; Caine, Benjamin; Vasudevan, Vijay; Han, Wei; Ngiam, Jiquan; Zhao, Hang; Timofeev, Aleksei; Ettinger, Scott; Krivokon, Maxim; Gao, Amy; Joshi, Aditya; Zhang, Yu; Shlens, Jonathon; Chen, Zhifeng; Anguelov, Dragomir

doi:10.1109/cvpr42600.2020.00252

Cited by 1,797 publications

(1,331 citation statements)

References 17 publications

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“…Waymo Open Dataset [85] is currently the largest dataset with LiDAR point clouds for autonomous driving. There are totally 798 training sequences with around 160k LiDAR samples, and 202 validation sequences with 40k LiDAR samples.…”

Section: Methodsmentioning

confidence: 99%

PV-RCNN: Point-Voxel Feature Set Abstraction for 3D Object Detection

Shi

Guo²,

Jiang³

et al. 2020

2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

1,247

1,045

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3D object detection is receiving increasing attention from both industry and academia thanks to its wide applications in various fields. In this paper, we propose the Point-Voxel Region based Convolution Neural Networks (PV-RCNNs) for accurate 3D detection from point clouds. First, we propose a novel 3D object detector, PV-RCNN-v1, which employs the voxel-to-keypoint scene encoding and keypoint-to-grid RoI feature abstraction two novel steps. These two steps deeply incorporate both 3D voxel CNN and PointNet-based set abstraction for learning discriminative point-cloud features. Second, we propose a more advanced framework, PV-RCNN-v2, for more efficient and accurate 3D detection. It consists of two major improvements, where the first one is the sectorized proposal-centric strategy for efficiently producing more representative and uniformly distributed keypoints, and the second one is the VectorPool aggregation to replace set abstraction for better aggregating local point-cloud features with much less resource consumption. With these two major modifications, our PV-RCNN-v2 runs more than twice as fast as the v1 version while still achieving better performance on the large-scale Waymo Open Dataset with 150m × 150m detection range. Extensive experiments demonstrate that our proposed PV-RCNNs significantly outperform previous state-of-the-art 3D detection methods on both the Waymo Open Dataset and the highly-competitive KITTI benchmark.

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Section: Methodsmentioning

confidence: 99%

PV-RCNN: Point-Voxel Feature Set Abstraction for 3D Object Detection

Shi

Guo²,

Jiang³

et al. 2020

2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

1,247

1,045

View full text Add to dashboard Cite

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“…The simulation results are clear: combining radiance and depth information outperforms radiance or depth alone. In this section, we ask whether we find the same pattern of results using publicly available radiance and depth data provided by Waymo [35]. Figure 9 shows renderings of the combined radiance and depth data (RGD) that we constructed from that dataset.…”

Section: Simulations Are Validated Using Real-world Datamentioning

confidence: 79%

“…The Waymo dataset [35] consists of 1150 video sequences (20 sec) of different scenes. It comprises well registered LiDAR and camera data.…”

Section: Real-world Dataset (Waymo)mentioning

confidence: 99%

ISETAuto: Detecting Vehicles With Depth and Radiance Information

2021

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“…In [33], a programmatic set of procedures was provided to convert the tracking data of nuScenes [34] to a new data set for trajectory forecasting while a new model was designed to address the lack of diversity and admissibility for trajectory forecasting through the understanding of the multimodal environmental context. In [35], a new data set covered 1150 scenes in Mountain View, Phoenix, and San Francisco with high‐quality LiDAR calibrator and camera device was proposed by the scientists and engineers from Google and Waymo. This work aims at minimising the gap of autonomous driving research between the industrial field and the academic field.…”

Section: Related Workmentioning

confidence: 99%

Trajectory prediction for intelligent vehicles using spatial‐attention mechanism

Yan

Zifeng

Yin

et al. 2020

IET intell. transp. syst

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It is of great interest for autonomous vehicles to predict the trajectory of other vehicles when planning a safe trajectory. To accurately predict the trajectory of the target vehicle, the interaction between vehicles must be considered. Interaction aware prediction methods track the previous trajectories of both the target vehicle and its surrounding vehicles. In this study, the authors consider trajectory prediction as a sequence‐to‐sequence prediction problem. They tackle this problem with an LSTM encoder–decoder framework. Moreover, they propose two spatial‐attention mechanisms to account for the interaction between vehicles, i.e. context attention and lane attention. Spatial‐attention mechanisms adopt the selective‐attention mechanism of human drivers. They choose context vectors to help the model understand the surrounding environment better and thus improve its prediction accuracy. They evaluate the authors’ methods on the highD data set recorded in German highways with root mean squared error metric. Their experimental results show superior performance to other state‐of‐the‐art methods. Code is available at https://github.com/momo1986/Spatial-attention.

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Scalability in Perception for Autonomous Driving: Waymo Open Dataset

Cited by 1,797 publications

References 17 publications

PV-RCNN: Point-Voxel Feature Set Abstraction for 3D Object Detection

PV-RCNN: Point-Voxel Feature Set Abstraction for 3D Object Detection

ISETAuto: Detecting Vehicles With Depth and Radiance Information

Trajectory prediction for intelligent vehicles using spatial‐attention mechanism

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