End-to-end Learning of Driving Models from Large-scale Video Datasets
Proposes an FCN-LSTM-based end-to-end driving model trained on large-scale crowd-sourced videos, achieving 84.1% action prediction accuracy.
Key Findings
Methodology
This paper introduces a unified FCN-LSTM architecture for future vehicle egomotion prediction, trained on extensive unlabeled driving videos. Inputs include monocular camera images and vehicle states; outputs are probabilistic multi-modal trajectories. Scene segmentation is integrated as an auxiliary task via privileged learning, improving feature representation. The dataset, BDDV, contains over 10,000 hours of diverse urban, highway, and rural driving data, surpassing existing datasets. Optimization uses cross-entropy and perplexity metrics, with ablation studies confirming the effectiveness of scene segmentation and temporal fusion modules. The model demonstrates superior offline prediction performance across various scenarios, validating its generalization and robustness.
Key Results
- Achieved 84.1% accuracy in predicting discrete driving actions, with perplexity reduced to 0.430, outperforming baseline models by significant margins.
- Incorporating scene segmentation accelerated training by 20%, and improved model performance on complex scenarios like intersections and traffic lights.
- For continuous lane following, a data-driven discretization method outperformed linear and log-space binning, significantly enhancing future angular speed predictions.
Significance
This work advances autonomous vehicle perception by leveraging large-scale, real-world crowd-sourced data, moving beyond traditional calibration-dependent models. It introduces a scalable, end-to-end framework capable of generalizing across diverse environments, addressing the critical challenge of robustness in real-world deployment. The integration of scene understanding as a privileged task enhances model interpretability and training efficiency, paving the way for safer, more adaptable autonomous systems. Its open dataset and architecture foster further research, accelerating progress in vision-based motion prediction.
Technical Contribution
The core innovation is the FCN-LSTM architecture that fuses pixel-level scene understanding with temporal sequence modeling, enabling multi-modal future trajectory prediction. The model incorporates a novel privileged learning paradigm, jointly optimizing motion and scene segmentation tasks, leading to improved feature robustness. The large-scale BDDV dataset provides a rich resource for training and benchmarking, supporting scalable, data-driven autonomous driving research. The approach demonstrates how multi-task learning and deep sequence modeling can be combined effectively for complex perception tasks.
Novelty
This is the first work to integrate FCN and LSTM in a unified, end-to-end framework for large-scale, real-world driving data, emphasizing multi-modal, multi-task learning. Unlike prior methods limited to simulation or small datasets, this approach leverages crowdsourced videos to learn a generalizable motion model. The use of scene segmentation as a privileged auxiliary task to enhance egomotion prediction is a key novelty, enabling faster training and better performance in complex scenarios.
Limitations
- The current model is trained offline and has not yet been integrated into a real vehicle, so real-time performance and control remain untested.
- Handling multi-agent interactions and unpredictable behaviors like pedestrians or sudden obstacles needs further development.
- High reliance on large annotated datasets poses scalability challenges; unsupervised or semi-supervised extensions are necessary for broader deployment.
Future Work
Future efforts will focus on deploying the model in real autonomous vehicles, integrating reinforcement learning for decision-making, and modeling multi-agent interactions. Exploring unsupervised learning techniques could reduce data annotation costs. Additionally, combining sensor fusion with visual inputs will improve robustness under adverse conditions. These directions aim to transition from offline prediction to real-time control in complex traffic environments.
AI Executive Summary
Autonomous driving has long been hindered by the limited scope of perception-action models trained on small, often simulated datasets. While rule-based systems achieved initial success, they struggled with complex, unpredictable scenarios. Recent advances in deep learning have demonstrated the potential of neural networks to interpret visual data and support basic driving tasks, but these methods often rely on calibration-dependent data or limited environments. To address these challenges, this paper introduces a novel FCN-LSTM architecture trained on a large-scale, crowdsourced driving video dataset (BDDV), comprising over 10,000 hours of diverse urban, highway, and rural scenes.
The core idea is to predict a probabilistic distribution over future vehicle egomotion based on monocular camera input and previous vehicle states. The model leverages pixel-level scene segmentation as an auxiliary task through privileged learning, which enhances feature representation and accelerates training. This multi-task approach allows the model to learn richer scene understanding and dynamic motion patterns simultaneously.
Experimental results show that the proposed model achieves 84.1% accuracy in discrete action prediction, with a perplexity of 0.430, outperforming baseline models such as CNN-only or speed-only predictors. The integration of scene segmentation improves training efficiency by approximately 20%, and the model demonstrates strong generalization across various driving scenarios, including intersections and traffic lights.
This work marks a significant step toward scalable, data-driven autonomous driving systems capable of operating safely in complex real-world environments. The open dataset and architecture lay a foundation for future research, including real-time deployment, multi-agent interaction modeling, and unsupervised learning strategies. Overall, it paves the way for more robust, adaptable, and scalable perception-action models that can meet the demands of future intelligent transportation systems.
Deep Dive
Key Concepts
Egocontrol
车辆的自我运动状态预测，是自主驾驶中的核心任务，基于视觉和传感器数据推断未来轨迹。
FCN (Fully Convolutional Network)
一种像素级场景理解的深度网络，用于提取场景特征，支持场景分割任务。
LSTM (Long Short-Term Memory)
一种序列建模的循环神经网络，能捕获时间序列中的长短期依赖关系，适合动态场景预测。
Privileged Learning
在训练中利用额外的场景信息（如场景分割标签）提升模型性能，测试时不依赖这些信息。
Perplexity
衡量模型预测序列的难度，数值越低代表模型越能准确预测未来动作，类似语言模型的困惑度指标。
Open Questions Unanswered questions from this research
- 1 如何将模型从离线预测迁移到实时控制，确保反应速度和安全性。
- 2 多智能体交互建模不足，未来需考虑多车、多行人场景的复杂动态。
- 3 大规模无标注数据的利用策略仍待探索，以降低数据标注成本。
Abstract
Robust perception-action models should be learned from training data with diverse visual appearances and realistic behaviors, yet current approaches to deep visuomotor policy learning have been generally limited to in-situ models learned from a single vehicle or a simulation environment. We advocate learning a generic vehicle motion model from large scale crowd-sourced video data, and develop an end-to-end trainable architecture for learning to predict a distribution over future vehicle egomotion from instantaneous monocular camera observations and previous vehicle state. Our model incorporates a novel FCN-LSTM architecture, which can be learned from large-scale crowd-sourced vehicle action data, and leverages available scene segmentation side tasks to improve performance under a privileged learning paradigm.