The NuPlan Benchmark for Real-World Autonomous Driving

Concepts de base

Machine learning has transformed perception and prediction in autonomous vehicles, but learning-based planning lags behind due to challenges in generalization and safety. The authors introduce the nuPlan benchmark to address these issues and evaluate planners' performance.

Résumé

The content discusses the slow adoption of machine learning-based techniques in autonomous vehicle planning compared to perception and prediction. It introduces the nuPlan benchmark, a dataset designed to test ML-based planners' ability to handle diverse driving scenarios safely and efficiently. The dataset includes 1282 hours of driving data from four cities, auto-labeled object tracks, traffic light data, and a simulation framework for evaluation. The authors analyze various baselines, highlighting gaps between ML-based and traditional methods.

Stats

We release the largest dataset for autonomous driving to date, with a total of 1282h from 4 cities.
We also publish an unprecedented 128h of sensor data.
We develop techniques to auto-label the dataset with accurate object tracks, traffic lights, and scenario labels.
The closed-loop simulation evaluates lawfulness, compliance with traffic rules, progress towards goals, rider comfort, speed limit compliance, and more.
A higher score indicates better planner performance across different scenarios.

Citations

"Machine Learning has replaced handcrafted methods for perception and prediction in autonomous vehicles."
"We introduce a new large-scale dataset that consists of 1282 hours of diverse driving scenarios from 4 cities."
"The closed-loop simulation evaluates lawfulness and compliance with traffic rules consisting of no at-fault collisions."
"The final score of a planner is computed by averaging the scores for its generated trajectories across all scenarios."
"Hybrid planners with learned-based components show the most promise in handling difficult scenarios."

Idées clés tirées de

Towards learning-based planning

by Napat Karnch... à arxiv.org 03-08-2024

https://arxiv.org/pdf/2403.04133.pdf

Questions plus approfondies

How can machine learning models be improved to overcome distribution shifts in closed-loop settings

To improve machine learning models' performance in overcoming distribution shifts in closed-loop settings, several strategies can be implemented:

Data Augmentation: By introducing variations and perturbations to the training data, ML models can learn to adapt to different scenarios more effectively. This approach helps expose the model to a wider range of situations, reducing the impact of distribution shifts.

Domain Adaptation Techniques: Utilizing domain adaptation methods such as adversarial training or self-supervised learning can help align the distributions between training and testing data. These techniques aim to make the model robust against changes in input distributions.

Ensemble Learning: Employing ensemble methods by combining multiple ML models trained on diverse datasets or with different architectures can enhance generalization capabilities. Ensemble models often perform better at handling distribution shifts compared to individual models.

Transfer Learning: Leveraging pre-trained models on related tasks or domains before fine-tuning them on specific closed-loop scenarios can help accelerate learning and improve performance when faced with distribution shifts.

Hybrid Approaches: Integrating rule-based components into ML-based planners can provide a balance between learned behavior and predefined rules, enhancing adaptability while maintaining safety and reliability in real-world applications.

What are the implications of rule-based planners outperforming purely ML-based ones in real-world applications

The fact that rule-based planners outperform purely ML-based ones in real-world applications has significant implications for autonomous driving systems:

Safety Assurance: Rule-based planners are often designed based on well-defined principles and regulations, ensuring adherence to traffic laws and safety guidelines without relying solely on learned behaviors that may lack interpretability.

Reliability Under Uncertainty: In complex environments where unexpected events occur, rule-based systems offer predictable responses based on predefined rules, providing stability even when facing novel situations not encountered during training.

Interpretability & Explainability: Rule-based approaches are inherently interpretable as they follow explicit logic or decision-making processes, making it easier for developers and regulators to understand how decisions are made within the system.

Robustness Against Distribution Shifts: Due to their deterministic nature, rule-based planners tend to handle distribution shifts better than purely ML-driven approaches by relying less on implicit patterns learned from data.

How can end-to-end planner training directly from sensor data impact autonomous driving systems

End-to-end planner training directly from sensor data could revolutionize autonomous driving systems by offering several benefits:

Efficiency & Simplicity:

Eliminates the need for separate modules like perception, prediction, planning by integrating all tasks into one end-to-end framework.
Reduces complexity in system design and maintenance by streamlining the entire process from raw sensor inputs to actionable plans.

Improved Adaptability:

Enables direct learning of driving policies from sensor data without manual feature engineering or task-specific algorithms.
Enhances adaptability as the model learns directly from environmental cues captured through sensors rather than relying on handcrafted features.

3.. Enhanced Generalization:
* Facilitates better generalization across diverse driving scenarios due
To its ability To learn directly From raw sensor inputs
* Improves performance under varying conditions And environments
By capturing relevant information directly From sensors
4.. Real-time Decision Making:
* Allows For quicker response times As there is no need For sequential processing stages
* Enables faster decision-making processes leading To improved overall System efficiency
5.. Continuous Learning:
* Supports continuous improvement Through online learning mechanisms
* Facilitates adaptive behavior over time Based On new experiences And feedback