Autonomous Driving Behavior Classification and Annotation
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Solution Overview
Problem
Autonomous driving systems fail to fully replicate human driving behavior, including attentive reactions and casual maneuvers, limiting their adaptability in dynamic environments.
Innovation Solution
A computer-implemented method and system that classify driving maneuvers as goal-oriented or stimulus-driven actions, determine the cause of stimulus-driven actions, and identify attention-capturing traffic objects, building a naturalistic driving behavior dataset to control vehicles autonomously, utilizing a four-layer annotation scheme and machine learning techniques.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If autonomous driving systems use real-time sensor data for vehicle control, then the system can respond to objects, roadways, and obstacles in real-time, but the system fails to replicate human driving behavior including attentive reactions and casual maneuvers
Solution Approach 1:
The patent segments driving behavior into two distinct categories: goal-oriented actions (planned maneuvers) and stimulus-driven actions (reactive responses). This segmentation allows the system to process different types of driving behaviors through specialized modules, capturing human-like driving patterns without requiring a complete overhaul of the autonomous driving architecture. The segmentation enables the system to handle complex human behavior replication through manageable, modular components.
2Reliability
If the system classifies driving maneuvers and builds comprehensive naturalistic driving behavior datasets with multiple annotations, then the system can execute more naturalistic driving behaviors, but the data processing and annotation complexity increases
Solution Approach 1:
The annotation process is segmented into four distinct layers: (1) driving maneuver classification (goal-oriented vs. stimulus-driven), (2) cause identification for stimulus-driven actions, (3) attention capturing traffic object identification, and (4) naturalistic behavior dataset compilation. This layered segmentation breaks down the complex annotation task into manageable stages, each with specific objectives and output formats, making the overall process more systematic and less overwhelming.
Solution Approach 2:
The patent adds multiple annotation dimensions to the driving behavior data: temporal dimension (sequence of actions), causal dimension (cause-effect relationships), attention dimension (traffic objects that captured driver attention), and classification dimension (goal-oriented vs. stimulus-driven). By annotating data across multiple dimensions simultaneously, the system creates a rich, multi-faceted dataset that captures the complexity of human driving behavior without requiring exponentially more data collection effort.
3Loss of information
If the system determines cause and attention capturing traffic objects for stimulus-driven actions, then the system can better understand human driving reactions, but the computational processing time increases
Solution Approach 1:
The system performs preliminary classification of driving maneuvers into goal-oriented and stimulus-driven categories. This preliminary action allows the system to quickly route different types of maneuvers through appropriate processing paths. For stimulus-driven actions, the system then activates more intensive processing to determine causes and attention objects, while goal-oriented actions follow a more direct path. This preliminary classification reduces overall processing time by avoiding unnecessary deep analysis for all maneuvers.
Solution Approach 2:
The system applies partial analysis based on the type of driving maneuver detected. For stimulus-driven actions, the system performs full analysis including cause determination and attention object identification. For goal-oriented actions, the system applies a more streamlined processing path that skips certain analysis steps. This selective application of processing depth optimizes the balance between complete scene understanding and processing efficiency.
Data Source
AI summary
A system and method for learning and executing naturalistic driving behavior that include classifying a driving maneuver as a goal-oriented action or a stimulus-driven action based on data associated with a trip of a vehicle. The system and method also include determining a cause associated with the driving maneuver classified as a stimulus-driven action and determining an attention capturing traffic related object associated with the driving maneuver. The system and method additionally include building a naturalistic driving behavior data set that includes at least one of: an annotation of the driving maneuver based on a classification of the driving maneuver, an annotation of the cause, and an annotation of the attention capturing traffic object. The system and method further include controlling the vehicle to be autonomously driven based on the naturalistic driving behavior data set.