Explainable Prediction of Pedestrian Crossing Actions and Vehicle Lane Change Maneuvers using Knowledge Graphs and Large Language Models

Core Concepts

The proposed approach integrates the reasoning abilities of Knowledge Graphs (KG) and the expressiveness capabilities of Large Language Models (LLM) to provide explainable predictions of pedestrian crossing actions and vehicle lane change maneuvers.

Abstract

The article presents an explainable road users' behavior prediction system that combines Knowledge Graph Embeddings (KGE) and Bayesian inference to enable fully inductive reasoning. The system leverages the semantic information contained in the road scene to issue predictions that rely on both legacy information in the graph and current evidence from sensors.
Two use cases are implemented:

Prediction of pedestrians' crossing actions:

Features like motion activity, proximity to the road, distance to the ego-vehicle, body orientation, and gaze direction are extracted and transformed into linguistic values.
A pedestrian behavior ontology (PedFeatKG) is built to represent the extracted features and their relationships.
KGE and Bayesian inference are used to predict the pedestrian's crossing intention.
Fuzzy rules are integrated into the KG (PedFeatRulesKG) to provide additional explainability.
The Retrieval Augmented Generation (RAG) technique is used to generate natural language explanations for the predictions.

Prediction of lane change maneuvers:

Vehicle features like lateral acceleration, lateral velocity, and Time-to-Collision (TTC) with surrounding vehicles are extracted and transformed into linguistic values.
A driver behavior ontology (DriverKG) is built to represent the extracted features and their relationships.
KGE and Bayesian inference are used to predict the vehicle's lane change intention.
The RAG technique is used to generate natural language explanations for the predictions.

The performance of the proposed approach surpasses the current state-of-the-art in both use cases, demonstrating the benefits of integrating contextual information and human knowledge into a knowledge-based representation for behavior prediction.

Stats

53% of all road traffic fatalities involve Vulnerable Road Users (VRUs)
23% of fatal accidents involve pedestrians
20% of road traffic fatalities in the European Union involve pedestrians
33% of all road crashes take place during a lane change maneuver

Quotes

"The ability to characterize and predict the behavior and motion patterns of road users, namely drivers and vulnerable road users (pedestrians and cyclists), as well as the explanation and understanding of the factors that rule the interactions among them, is essential for increasing road safety and traffic efficiency in the context of autonomous driving."
"Accounting for contextual information is essential for understanding behavior in this situation. This context-based reasoning approach can be extended and applied to many other similar situations, involving pedestrians and drivers, where contextual information is key for understanding and anticipating behavior."

Key Insights Distilled From

RAG-based Explainable Prediction of Road Users Behaviors for Automated Driving using Knowledge Graphs and Large Language Models

by Moha... at arxiv.org 05-02-2024

https://arxiv.org/pdf/2405.00449.pdf

Deeper Inquiries

How can the proposed approach be extended to incorporate additional contextual information, such as weather conditions, traffic signals, and social norms, to further improve the accuracy and explainability of the predictions

To enhance the accuracy and explainability of predictions in the proposed approach, additional contextual information such as weather conditions, traffic signals, and social norms can be incorporated in the following ways:

Weather Conditions: Including weather data like rain, fog, or snow can significantly impact road users' behaviors. By integrating weather APIs or sensors in the autonomous driving system, the model can adjust predictions based on weather conditions. For instance, pedestrians might behave differently in rainy weather, crossing roads more cautiously.

Traffic Signals: Incorporating real-time traffic signal data can provide valuable insights into road users' behaviors. Understanding how pedestrians and drivers react to traffic signals like red lights, green lights, or pedestrian crossing signals can improve the prediction accuracy. This information can be obtained from traffic management systems or IoT devices.

Social Norms: Considering social norms in behavior prediction is crucial for understanding human behavior on the road. Different cultures and regions have varying norms that influence road users' actions. By integrating social norm data into the knowledge graph, the model can adapt predictions based on cultural influences.

By expanding the knowledge graph to include these additional contextual factors and leveraging Bayesian inference to analyze the relationships between these factors and road users' behaviors, the system can provide more accurate and explainable predictions in autonomous driving scenarios.

What are the potential limitations of the Bayesian inference approach used in this work, and how could alternative reasoning techniques, such as fuzzy logic or Markov decision processes, be explored to enhance the system's decision-making capabilities

The Bayesian inference approach used in the work has certain limitations that could be addressed by exploring alternative reasoning techniques like fuzzy logic or Markov decision processes:

Limitations of Bayesian Inference:

Complexity: Bayesian inference can become computationally expensive with a large number of variables and dependencies, impacting real-time decision-making.
Assumptions: The approach relies on strong assumptions about the underlying distributions of data, which may not always hold true in real-world scenarios.
Interpretability: While Bayesian inference provides probabilistic reasoning, the explanations generated may not always be intuitive or easily understandable by non-experts.

Alternative Techniques:

Fuzzy Logic: Fuzzy logic allows for handling uncertainty and imprecision in data, making it suitable for modeling human decision-making processes. By incorporating fuzzy rules into the system, it can provide more nuanced and context-aware predictions.
Markov Decision Processes (MDPs): MDPs are useful for modeling sequential decision-making processes. By integrating MDPs into the system, it can account for the dynamic nature of road user behaviors and optimize decision-making over time.

Exploring these alternative reasoning techniques can enhance the system's decision-making capabilities by addressing the limitations of Bayesian inference and providing more robust and adaptable models for autonomous driving scenarios.

How could the integration of the knowledge-based reasoning and the deep learning components be further optimized to achieve a more seamless and efficient workflow, potentially leading to real-time applications in autonomous driving scenarios

To optimize the integration of knowledge-based reasoning and deep learning components for real-time applications in autonomous driving scenarios, the following strategies can be implemented:

Efficient Data Processing:

Implement data streaming techniques to process real-time sensor data and update the knowledge graph dynamically.
Utilize parallel processing and distributed computing to handle the computational load of both knowledge-based reasoning and deep learning tasks simultaneously.

Model Optimization:

Fine-tune deep learning models to improve prediction speed without compromising accuracy.
Implement incremental learning techniques to update the knowledge graph and model parameters in real-time based on new data.

Hybrid Models:

Develop hybrid models that combine the strengths of knowledge-based reasoning and deep learning for more comprehensive decision-making.
Utilize ensemble learning techniques to integrate predictions from multiple models for improved accuracy and robustness.

By optimizing data processing, model efficiency, and leveraging hybrid models, the integration of knowledge-based reasoning and deep learning components can be streamlined for real-time applications in autonomous driving scenarios, ensuring timely and accurate decision-making.