End-to-End Predictive Planner for Autonomous Driving with Consistency Models

• URL: http://arxiv.org/abs/2502.08033v1
• Date: Wed, 12 Feb 2025 00:26:01 GMT
• Title: End-to-End Predictive Planner for Autonomous Driving with Consistency Models
• Authors: Anjian Li, Sangjae Bae, David Isele, Ryne Beeson, Faizan M. Tariq,
• Abstract summary: Trajectory prediction and planning are fundamental components for autonomous vehicles to navigate safely and efficiently in dynamic environments. Traditionally, these components have often been treated as separate modules, limiting the ability to perform interactive planning. We present a novel unified and data-driven framework that integrates prediction and planning with a single consistency model.
• Score: 5.966385886363771
• License:
• Abstract: Trajectory prediction and planning are fundamental components for autonomous vehicles to navigate safely and efficiently in dynamic environments. Traditionally, these components have often been treated as separate modules, limiting the ability to perform interactive planning and leading to computational inefficiency in multi-agent scenarios. In this paper, we present a novel unified and data-driven framework that integrates prediction and planning with a single consistency model. Trained on real-world human driving datasets, our consistency model generates samples from high-dimensional, multimodal joint trajectory distributions of the ego and multiple surrounding agents, enabling end-to-end predictive planning. It effectively produces interactive behaviors, such as proactive nudging and yielding to ensure both safe and efficient interactions with other road users. To incorporate additional planning constraints on the ego vehicle, we propose an alternating direction method for multi-objective guidance in online guided sampling. Compared to diffusion models, our consistency model achieves better performance with fewer sampling steps, making it more suitable for real-time deployment. Experimental results on Waymo Open Motion Dataset (WOMD) demonstrate our method's superiority in trajectory quality, constraint satisfaction, and interactive behavior compared to various existing approaches.

Related papers

• Characterized Diffusion Networks for Enhanced Autonomous Driving Trajectory Prediction [0.6202955567445396]
  We present a novel trajectory prediction model for autonomous driving. Our model enhances the accuracy and reliability of trajectory predictions by incorporating uncertainty estimation and complex agent interactions. The proposed model showcases strong potential for application in real-world autonomous driving systems.
  arXiv  Detail & Related papers  (2024-11-25T15:03:44Z)
• DiFSD: Ego-Centric Fully Sparse Paradigm with Uncertainty Denoising and Iterative Refinement for Efficient End-to-End Self-Driving [55.53171248839489]
  We propose an ego-centric fully sparse paradigm, named DiFSD, for end-to-end self-driving. Specifically, DiFSD mainly consists of sparse perception, hierarchical interaction and iterative motion planner. Experiments conducted on nuScenes and Bench2Drive datasets demonstrate the superior planning performance and great efficiency of DiFSD.
  arXiv  Detail & Related papers  (2024-09-15T15:55:24Z)
• Diffusion-Based Environment-Aware Trajectory Prediction [3.1406146587437904]
  The ability to predict the future trajectories of traffic participants is crucial for the safe and efficient operation of autonomous vehicles. In this paper, a diffusion-based generative model for multi-agent trajectory prediction is proposed. The model is capable of capturing the complex interactions between traffic participants and the environment, accurately learning the multimodal nature of the data.
  arXiv  Detail & Related papers  (2024-03-18T10:35:15Z)
• Trajeglish: Traffic Modeling as Next-Token Prediction [67.28197954427638]
  A longstanding challenge for self-driving development is simulating dynamic driving scenarios seeded from recorded driving logs. We apply tools from discrete sequence modeling to model how vehicles, pedestrians and cyclists interact in driving scenarios. Our model tops the Sim Agents Benchmark, surpassing prior work along the realism meta metric by 3.3% and along the interaction metric by 9.9%.
  arXiv  Detail & Related papers  (2023-12-07T18:53:27Z)
• PPAD: Iterative Interactions of Prediction and Planning for End-to-end Autonomous Driving [57.89801036693292]
  PPAD (Iterative Interaction of Prediction and Planning Autonomous Driving) considers the timestep-wise interaction to better integrate prediction and planning. We design ego-to-agent, ego-to-map, and ego-to-BEV interaction mechanisms with hierarchical dynamic key objects attention to better model the interactions.
  arXiv  Detail & Related papers  (2023-11-14T11:53:24Z)
• DICE: Diverse Diffusion Model with Scoring for Trajectory Prediction [7.346307332191997]
  We present a novel framework that leverages diffusion models for predicting future trajectories in a computationally efficient manner. We employ an efficient sampling mechanism that allows us to maximize the number of sampled trajectories for improved accuracy. We show the effectiveness of our approach by conducting empirical evaluations on common pedestrian (UCY/ETH) and autonomous driving (nuScenes) benchmark datasets.
  arXiv  Detail & Related papers  (2023-10-23T05:04:23Z)
• JFP: Joint Future Prediction with Interactive Multi-Agent Modeling for Autonomous Driving [12.460224193998362]
  We propose an end-to-end trainable model that learns directly the interaction between pairs of agents in a structured, graphical model formulation. Our approach improves significantly on the trajectory overlap metrics while obtaining on-par or better performance on single-agent trajectory metrics.
  arXiv  Detail & Related papers  (2022-12-16T20:59:21Z)
• Spatio-Temporal Graph Dual-Attention Network for Multi-Agent Prediction and Tracking [23.608125748229174]
  We propose a generic generative neural system for multi-agent trajectory prediction involving heterogeneous agents. The proposed system is evaluated on three public benchmark datasets for trajectory prediction.
  arXiv  Detail & Related papers  (2021-02-18T02:25:35Z)
• Instance-Aware Predictive Navigation in Multi-Agent Environments [93.15055834395304]
  We propose an Instance-Aware Predictive Control (IPC) approach, which forecasts interactions between agents as well as future scene structures. We adopt a novel multi-instance event prediction module to estimate the possible interaction among agents in the ego-centric view. We design a sequential action sampling strategy to better leverage predicted states on both scene-level and instance-level.
  arXiv  Detail & Related papers  (2021-01-14T22:21:25Z)
• Risk-Sensitive Sequential Action Control with Multi-Modal Human Trajectory Forecasting for Safe Crowd-Robot Interaction [55.569050872780224]
  We present an online framework for safe crowd-robot interaction based on risk-sensitive optimal control, wherein the risk is modeled by the entropic risk measure. Our modular approach decouples the crowd-robot interaction into learning-based prediction and model-based control. A simulation study and a real-world experiment show that the proposed framework can accomplish safe and efficient navigation while avoiding collisions with more than 50 humans in the scene.
  arXiv  Detail & Related papers  (2020-09-12T02:02:52Z)
• SMART: Simultaneous Multi-Agent Recurrent Trajectory Prediction [72.37440317774556]
  We propose advances that address two key challenges in future trajectory prediction. multimodality in both training data and predictions and constant time inference regardless of number of agents.
  arXiv  Detail & Related papers  (2020-07-26T08:17:10Z)

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.