TrajectoryNAS: A Neural Architecture Search for Trajectory Prediction

• URL: http://arxiv.org/abs/2403.11695v1
• Date: Mon, 18 Mar 2024 11:48:41 GMT
• Title: TrajectoryNAS: A Neural Architecture Search for Trajectory Prediction
• Authors: Ali Asghar Sharifi, Ali Zoljodi, Masoud Daneshtalab,
• Abstract summary: Trajectory prediction is a critical component of autonomous driving systems. This paper introduces TrajectoryNAS, a pioneering method that focuses on utilizing point cloud data for trajectory prediction.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Autonomous driving systems are a rapidly evolving technology that enables driverless car production. Trajectory prediction is a critical component of autonomous driving systems, enabling cars to anticipate the movements of surrounding objects for safe navigation. Trajectory prediction using Lidar point-cloud data performs better than 2D images due to providing 3D information. However, processing point-cloud data is more complicated and time-consuming than 2D images. Hence, state-of-the-art 3D trajectory predictions using point-cloud data suffer from slow and erroneous predictions. This paper introduces TrajectoryNAS, a pioneering method that focuses on utilizing point cloud data for trajectory prediction. By leveraging Neural Architecture Search (NAS), TrajectoryNAS automates the design of trajectory prediction models, encompassing object detection, tracking, and forecasting in a cohesive manner. This approach not only addresses the complex interdependencies among these tasks but also emphasizes the importance of accuracy and efficiency in trajectory modeling. Through empirical studies, TrajectoryNAS demonstrates its effectiveness in enhancing the performance of autonomous driving systems, marking a significant advancement in the field.Experimental results reveal that TrajcetoryNAS yield a minimum of 4.8 higger accuracy and 1.1* lower latency over competing methods on the NuScenes dataset.

Related papers

• VECTOR: Velocity-Enhanced GRU Neural Network for Real-Time 3D UAV Trajectory Prediction [2.1825723033513165]
  We propose a new trajectory prediction method using Gated Recurrent Units (GRUs) within sequence-based neural networks. We employ both synthetic and real-world 3D UAV trajectory data, capturing a wide range of flight patterns, speeds, and agility. The GRU-based models significantly outperform state-of-the-art RNN approaches, with a mean square error (MSE) as low as 2 x 10-8.
  arXiv  Detail & Related papers  (2024-10-24T07:16:42Z)
• GRANP: A Graph Recurrent Attentive Neural Process Model for Vehicle Trajectory Prediction [3.031375888004876]
  We propose a novel model named Graph Recurrent Attentive Neural Process (GRANP) for vehicle trajectory prediction. GRANP contains an encoder with deterministic and latent paths, and a decoder for prediction. We show that GRANP achieves state-of-the-art results and can efficiently quantify uncertainties.
  arXiv  Detail & Related papers  (2024-04-09T05:51:40Z)
• A Novel Deep Neural Network for Trajectory Prediction in Automated Vehicles Using Velocity Vector Field [12.067838086415833]
  This paper proposes a novel technique for trajectory prediction that combines a data-driven learning-based method with a velocity vector field (VVF) generated from a nature-inspired concept. The accuracy remains consistent with decreasing observation windows which alleviates the requirement of a long history of past observations for accurate trajectory prediction.
  arXiv  Detail & Related papers  (2023-09-19T22:14:52Z)
• Pre-training on Synthetic Driving Data for Trajectory Prediction [61.520225216107306]
  We propose a pipeline-level solution to mitigate the issue of data scarcity in trajectory forecasting. We adopt HD map augmentation and trajectory synthesis for generating driving data, and then we learn representations by pre-training on them. We conduct extensive experiments to demonstrate the effectiveness of our data expansion and pre-training strategies.
  arXiv  Detail & Related papers  (2023-09-18T19:49:22Z)
• Interaction-Aware Personalized Vehicle Trajectory Prediction Using Temporal Graph Neural Networks [8.209194305630229]
  Existing methods mainly rely on generic trajectory predictions from large datasets. We propose an approach for interaction-aware personalized vehicle trajectory prediction that incorporates temporal graph neural networks.
  arXiv  Detail & Related papers  (2023-08-14T20:20:26Z)
• An End-to-End Framework of Road User Detection, Tracking, and Prediction from Monocular Images [11.733622044569486]
  We build an end-to-end framework for detection, tracking, and trajectory prediction called ODTP. It adopts the state-of-the-art online multi-object tracking model, QD-3DT, for perception and trains the trajectory predictor, DCENet++, directly based on the detection results. We evaluate the performance of ODTP on the widely used nuScenes dataset for autonomous driving.
  arXiv  Detail & Related papers  (2023-08-09T15:46:25Z)
• Point Cloud Forecasting as a Proxy for 4D Occupancy Forecasting [58.45661235893729]
  One promising self-supervised task is 3D point cloud forecasting from unannotated LiDAR sequences. We show that this task requires algorithms to implicitly capture (1) sensor extrinsics (i.e., the egomotion of the autonomous vehicle), (2) sensor intrinsics (i.e., the sampling pattern specific to the particular LiDAR sensor), and (3) the shape and motion of other objects in the scene. We render point cloud data from 4D occupancy predictions given sensor extrinsics and intrinsics, allowing one to train and test occupancy algorithms with unannotated LiDAR sequences.
  arXiv  Detail & Related papers  (2023-02-25T18:12:37Z)
• Efficient and Robust LiDAR-Based End-to-End Navigation [132.52661670308606]
  We present an efficient and robust LiDAR-based end-to-end navigation framework. We propose Fast-LiDARNet that is based on sparse convolution kernel optimization and hardware-aware model design. We then propose Hybrid Evidential Fusion that directly estimates the uncertainty of the prediction from only a single forward pass.
  arXiv  Detail & Related papers  (2021-05-20T17:52:37Z)
• SLPC: a VRNN-based approach for stochastic lidar prediction and completion in autonomous driving [63.87272273293804]
  We propose a new LiDAR prediction framework that is based on generative models namely Variational Recurrent Neural Networks (VRNNs) Our algorithm is able to address the limitations of previous video prediction frameworks when dealing with sparse data by spatially inpainting the depth maps in the upcoming frames. We present a sparse version of VRNNs and an effective self-supervised training method that does not require any labels.
  arXiv  Detail & Related papers  (2021-02-19T11:56:44Z)
• IntentNet: Learning to Predict Intention from Raw Sensor Data [86.74403297781039]
  In this paper, we develop a one-stage detector and forecaster that exploits both 3D point clouds produced by a LiDAR sensor as well as dynamic maps of the environment. Our multi-task model achieves better accuracy than the respective separate modules while saving computation, which is critical to reducing reaction time in self-driving applications.
  arXiv  Detail & Related papers  (2021-01-20T00:31:52Z)
• PnPNet: End-to-End Perception and Prediction with Tracking in the Loop [82.97006521937101]
  We tackle the problem of joint perception and motion forecasting in the context of self-driving vehicles. We propose Net, an end-to-end model that takes as input sensor data, and outputs at each time step object tracks and their future level.
  arXiv  Detail & Related papers  (2020-05-29T17:57:25Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.