mlsanityData to 14 Oct 2026

Conditional Latent ODEs for Motion Prediction in Autonomous Driving

arXiv 2405.19183 ↗First posted 29 May 2024cs.ROcited by 2

On the map: End-to-end autonomous driving → Imitation-learning end-to-end planners

also in 1 more
Abstract (arXiv)

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 neural ODE. Our network architecture is inspired from the Latent-ODE model. The experiment shows that our method outperform the baseline methods in the simulation of multi-agent driving and is very efficient in term of GPU memory consumption. Our code and docker image are publicly available: https://github.com/TruongKhang/cLODE; https://hub.docker.com/r/kim4375731/clode.

Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Placed in Imitation-learning end-to-end planners, one of 9 directions in End-to-end autonomous driving. Builds on 11 of its 21 references that are in the map. First posted on arXiv 29 May 2024.

Builds on

11 of 21 references.

References · 11

  1. 2018Neural Ordinary Differential Equationscited by 3,779
  2. 2016Generative Adversarial Imitation Learningcited by 1,597
  3. 2016Recurrent Neural Networks for Multivariate Time Series with Missing Valuescited by 554
  4. 2018Behavioral Cloning from Observationcited by 492
  5. 2016Guided Cost Learning: Deep Inverse Optimal Control via Policy Optimizationcited by 406
  6. 2019Causal Confusion in Imitation Learningcited by 288
  7. 2018Multi-Agent Generative Adversarial Imitation Learningcited by 100
  8. 2017Imitating Driver Behavior with Generative Adversarial Networkscited by 93
  9. 2016Model-Free Imitation Learning with Policy Optimizationcited by 52
  10. 2018Multi-Agent Imitation Learning for Driving Simulationcited by 50
  11. 2024A Survey of Imitation Learning Methods, Environments and Metricscited by 16

Built on by

Cited by year

Citing papers per year: 2025: 2.

2 papers.

  1. 2025Generative AI for Autonomous Driving: Frontiers and Opportunitiescited by 7
  2. 2025High-order expansion of Neural Ordinary Differential Equations flowscited by 1

Similar, not cited

Close in wording, no citation either way

  1. 2019Safer End-to-End Autonomous Driving via Conditional Imitation Learning and Command Augmentation
  2. 2025LAP: Fast LAtent Diffusion Planner for Autonomous Driving
  3. 2024End-to-End Steering for Autonomous Vehicles via Conditional Imitation Co-Learning
  4. 2021Generative Adversarial Imitation Learning for End-to-End Autonomous Driving on Urban Environments
  5. 2024Mitigating Covariate Shift in Imitation Learning for Autonomous Vehicles Using Latent Space Generative World Models
  6. 2026Adaptive Time Step Flow Matching for Autonomous Driving Motion Planning
  7. 2023Addressing Limitations of State-Aware Imitation Learning for Autonomous Driving
  8. 2025Robust Planning for Autonomous Driving via Mixed Adversarial Diffusion Predictions