Naturalistic Driving Behavior Data Set for Autonomous Control
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Solution Overview
Problem
Autonomous driving systems fail to incorporate a driver's logic, attentive behavior, and casual reactions into their decision-making processes, limiting their ability to adapt to dynamic driving scenarios effectively.
Innovation Solution
A computer-implemented method and system that learns naturalistic driving behavior by analyzing vehicle dynamic data and image data to detect behavioral events, classify stimulus-driven actions, and build a naturalistic driving behavior data set, which is used to control the vehicle autonomously, incorporating a four-layer annotation scheme for goal-oriented and stimulus-driven actions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional sensor data processing is used for autonomous driving, then real-time object detection is achieved, but driver's logic and casual reactions are not incorporated
Solution Approach 1:
The patent segments driving behavior into distinct categories: stimulus-driven actions (reactive behaviors) and goal-oriented actions (proactive behaviors). This segmentation allows the system to process different types of driving behaviors separately, improving adaptability without overwhelming system complexity. The behavior data set is structured with separate annotations for each action type, enabling targeted analysis and control.
Solution Approach 2:
The patent adds a temporal dimension to traditional sensor data by incorporating sequential behavior data with timestamps. This allows the system to analyze not just what objects are present, but how drivers respond over time to various stimuli. The four-layer annotation scheme adds another dimension by categorizing behaviors at multiple levels of abstraction, from basic actions to complex decision-making patterns.
2Loss of information
If driver behavior analysis is incorporated into autonomous driving systems, then situational understanding is improved, but data processing complexity increases
Solution Approach 1:
The patent performs preliminary classification of driving behaviors into stimulus-driven and goal-oriented categories during data collection and annotation phases. This preliminary organization reduces the complexity of subsequent analysis by pre-sorting behavior data into manageable categories. The four-layer annotation scheme is applied in advance to structure the behavior data set, making it easier to process and query during autonomous operation.
Solution Approach 2:
The patent introduces a behavior data set as an intermediary layer between traditional sensor data and autonomous driving control. This behavior data set, with its structured annotations, serves as a mediator that translates raw driver behavior observations into actionable insights. The four-layer annotation structure acts as an intermediary framework that organizes complex behavior data into manageable categories for analysis and application.
3Measurement precision
If naturalistic driving behavior data set is built with detailed annotations, then behavior classification accuracy is improved, but data collection and processing time increases
Solution Approach 1:
The patent segments the annotation process into four distinct layers, each focusing on specific aspects of driver behavior. This segmentation allows annotators to work on different layers independently and in parallel, reducing overall processing time while maintaining high accuracy. Each layer can be validated separately, enabling efficient quality control without requiring complete re-annotation of entire data sets.
Solution Approach 2:
The patent applies annotation efforts selectively, focusing first on identifying and annotating stimulus-driven actions, which are critical for safety-critical responses. Goal-oriented actions are annotated with slightly less detail in initial processing passes. This partial annotation approach allows the system to achieve sufficient accuracy for critical functions without the time cost of exhaustive annotation of all behavior types.
Data Source
AI summary
A system and method for learning naturalistic driving behavior based on vehicle dynamic data that include receiving vehicle dynamic data and image data and analyzing the vehicle dynamic data and the image data to detect a plurality of behavioral events. The system and method also include classifying at least one behavioral event as a stimulus-driven action and building a naturalistic driving behavior data set that includes annotations that are based on the at least one behavioral event that is classified as the stimulus-driven action. The system and method further include controlling a vehicle to be autonomously driven based on the naturalistic driving behavior data set.