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  • Conditional Latent ODEs for Motion Prediction in Autonomous Driving
    Abstract: This paper addresses imitation learning for motion prediction problem in autonomous driving, especially in multi-agent setting Different from previous methods based on GAN, we present the conditional latent ordinary differential equation (cLODE) to leverage both the generative strength of conditional VAE and the continuous
  • Social Lode: Human Trajectory Prediction with Latent Odes
    Abstract: Human trajectory prediction is crucial in human-computer interaction and even in the safety of autonomous driving In this work, A new method, called Social Latent Ordinary Differential Equation (Social LODE), is introduced for predicting human trajectories
  • Conditional Latent ODEs for Motion Prediction in Autonomous Driving
    This paper addresses imitation learning for motion prediction problem in autonomous driving, especially in multi-agent setting Different from previous methods based on GAN, we present the conditional latent ordinary differential equation (cLODE) to leverage both the generative strength of conditional VAE and the continuous representation of
  • Conditional Latent ODEs for Motion Prediction in Autonomous Driving
    We present a novel motion prediction system for autonomous driving Our system is based on the Bayesian inverse planning framework, which efficiently orchestrates map-based goal extraction,
  • colorfulfuture Awesome-Trajectory-Motion-Prediction-Papers
    Enduring, Efficient and Robust Trajectory Prediction Attack in Autonomous Driving via Optimization-Driven Multi-Frame Perturbation Framework Paper Leveraging SD Map to Augment HD Map-based Trajectory Prediction
  • Conditional Latent ODEs for Motion Prediction in Autonomous Driving - 专知论文
    This paper addresses imitation learning for motion prediction problem in autonomous driving, especially in multi-agent setting Different from previous methods based on GAN, we present the conditional latent ordinary differential equation (cLODE) to leverage both the generative strength of conditional VAE and the continuous representation of
  • Conditional Latent ODEs for Motion Prediction in Autonomous Driving
    In this report, we proposed a generative imitation learning model for autonomous driving, the conditional latent ordinary differential equation (cLODE) cLODE combines VAE with neural ODE for a more accurate prediction of multi-agent vehicle trajectories
  • Conditional Latent ODEs for Motion Prediction in Autonomous Driving
    Article "Conditional Latent ODEs for Motion Prediction in Autonomous Driving" Detailed information of the J-GLOBAL is an information service managed by the Japan Science and Technology Agency (hereinafter referred to as "JST")
  • Efficient Baselines for Motion Prediction in Autonomous Driving
    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
  • Title: Conditional Latent ODEs for Motion Prediction in Autonomous Driving
    Abstract: This paper addresses imitation learning for motion prediction problem in autonomous driving, especially in multi-agent setting Different from previous methods based on GAN, we present the conditional latent ordinary differential equation (cLODE) to leverage both the generative strength of conditional VAE and the continuous





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