Efficient Baselines for Motion Prediction in Autonomous Driving

• URL: http://arxiv.org/abs/2309.03387v2
• Date: Tue, 31 Oct 2023 22:14:51 GMT
• Title: Efficient Baselines for Motion Prediction in Autonomous Driving
• Authors: Carlos G\'omez-Hu\'elamo, Marcos V. Conde, Rafael Barea, Manuel Oca\~na, Luis M. Bergasa
• Abstract summary: Motion Prediction (MP) of multiple surroundings agents is a crucial task in arbitrarily complex environments. We aim to develop compact models using State-Of-The-Art (SOTA) techniques for MP, including attention mechanisms and GNNs.
• Score: 7.608073471097835
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Motion Prediction (MP) of multiple surroundings agents is a crucial task in arbitrarily complex environments, from simple robots to Autonomous Driving Stacks (ADS). Current techniques tackle this problem using end-to-end pipelines, where the input data is usually a rendered top-view of the physical information and the past trajectories of the most relevant agents; leveraging this information is a must to obtain optimal performance. In that sense, a reliable ADS must produce reasonable predictions on time. However, despite many approaches use simple ConvNets and LSTMs to obtain the social latent features, State-Of-The-Art (SOTA) models might be too complex for real-time applications when using both sources of information (map and past trajectories) as well as little interpretable, specially considering the physical information. Moreover, the performance of such models highly depends on the number of available inputs for each particular traffic scenario, which are expensive to obtain, particularly, annotated High-Definition (HD) maps. In this work, we propose several efficient baselines for the well-known Argoverse 1 Motion Forecasting Benchmark. We aim to develop compact models using SOTA techniques for MP, including attention mechanisms and GNNs. Our lightweight models use standard social information and interpretable map information such as points from the driveable area and plausible centerlines by means of a novel preprocessing step based on kinematic constraints, in opposition to black-box CNN-based or too-complex graphs methods for map encoding, to generate plausible multimodal trajectories achieving up-to-pair accuracy with less operations and parameters than other SOTA methods. Our code is publicly available at https://github.com/Cram3r95/mapfe4mp .

Related papers

• SIMPL: A Simple and Efficient Multi-agent Motion Prediction Baseline for Autonomous Driving [27.776472262857045]
  This paper presents a Simple and effIcient Motion Prediction baseLine (SIMPL) for autonomous vehicles. We propose a compact and efficient global feature fusion module that performs directed message passing in a symmetric manner. As a strong baseline, SIMPL exhibits highly competitive performance on Argoverse 1 & 2 motion forecasting benchmarks.
  arXiv  Detail & Related papers  (2024-02-04T15:07:49Z)
• JRDB-Traj: A Dataset and Benchmark for Trajectory Forecasting in Crowds [79.00975648564483]
  Trajectory forecasting models, employed in fields such as robotics, autonomous vehicles, and navigation, face challenges in real-world scenarios. This dataset provides comprehensive data, including the locations of all agents, scene images, and point clouds, all from the robot's perspective. The objective is to predict the future positions of agents relative to the robot using raw sensory input data.
  arXiv  Detail & Related papers  (2023-11-05T18:59:31Z)
• A Fast and Map-Free Model for Trajectory Prediction in Traffics [2.435517936694533]
  This paper proposes an efficient trajectory prediction model that is not dependent on traffic maps. By comprehensively utilizing attention mechanism, LSTM, graph convolution network and temporal transformer, our model is able to learn rich dynamic and interaction information of all agents. Our model achieves the highest performance when comparing with existing map-free methods and also exceeds most map-based state-of-the-art methods on the Argoverse dataset.
  arXiv  Detail & Related papers  (2023-07-19T08:36:31Z)
• Exploring Attention GAN for Vehicle Motion Prediction [2.887073662645855]
  We study the influence of attention in generative models for motion prediction, considering both physical and social context. We validate our method using the Argoverse Motion Forecasting Benchmark 1.1, achieving competitive unimodal results.
  arXiv  Detail & Related papers  (2022-09-26T13:18:32Z)
• Conditioned Human Trajectory Prediction using Iterative Attention Blocks [70.36888514074022]
  We present a simple yet effective pedestrian trajectory prediction model aimed at pedestrians positions prediction in urban-like environments. Our model is a neural-based architecture that can run several layers of attention blocks and transformers in an iterative sequential fashion. We show that without explicit introduction of social masks, dynamical models, social pooling layers, or complicated graph-like structures, it is possible to produce on par results with SoTA models.
  arXiv  Detail & Related papers  (2022-06-29T07:49:48Z)
• Exploring Map-based Features for Efficient Attention-based Vehicle Motion Prediction [3.222802562733787]
  Motion prediction of multiple agents is a crucial task in arbitrarily complex environments. We show how to achieve competitive performance on the Argoverse 1.0 Benchmark using efficient attention-based models.
  arXiv  Detail & Related papers  (2022-05-25T22:38:11Z)
• SOLIS -- The MLOps journey from data acquisition to actionable insights [62.997667081978825]
  In this paper we present a unified deployment pipeline and freedom-to-operate approach that supports all requirements while using basic cross-platform tensor framework and script language engines. This approach however does not supply the needed procedures and pipelines for the actual deployment of machine learning capabilities in real production grade systems.
  arXiv  Detail & Related papers  (2021-12-22T14:45:37Z)
• Learnable Online Graph Representations for 3D Multi-Object Tracking [156.58876381318402]
  We propose a unified and learning based approach to the 3D MOT problem. We employ a Neural Message Passing network for data association that is fully trainable. We show the merit of the proposed approach on the publicly available nuScenes dataset by achieving state-of-the-art performance of 65.6% AMOTA and 58% fewer ID-switches.
  arXiv  Detail & Related papers  (2021-04-23T17:59:28Z)
• Injecting Knowledge in Data-driven Vehicle Trajectory Predictors [82.91398970736391]
  Vehicle trajectory prediction tasks have been commonly tackled from two perspectives: knowledge-driven or data-driven. In this paper, we propose to learn a "Realistic Residual Block" (RRB) which effectively connects these two perspectives. Our proposed method outputs realistic predictions by confining the residual range and taking into account its uncertainty.
  arXiv  Detail & Related papers  (2021-03-08T16:03:09Z)
• Trajformer: Trajectory Prediction with Local Self-Attentive Contexts for Autonomous Driving [13.861631911491651]
  Self-attention enables better control over representing the agent's social context. We show improvements on standard metrics over various baselines on the Argoverse dataset.
  arXiv  Detail & Related papers  (2020-11-30T15:42:15Z)
• Multi-scale Interaction for Real-time LiDAR Data Segmentation on an Embedded Platform [62.91011959772665]
  Real-time semantic segmentation of LiDAR data is crucial for autonomously driving vehicles. Current approaches that operate directly on the point cloud use complex spatial aggregation operations. We propose a projection-based method, called Multi-scale Interaction Network (MINet), which is very efficient and accurate.
  arXiv  Detail & Related papers  (2020-08-20T19:06:11Z)

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.