Vehicle Behavior Profiles for Accurate Abnormal Driving Detection
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Solution Overview
Problem
Existing systems struggle to accurately differentiate between abnormal and normal driving behaviors in vehicles, as certain behaviors are reasonable for specific types of vehicles but deemed abnormal for others.
Innovation Solution
A system that classifies remote vehicles based on their type, retrieves a behavior profile specific to that type, and compares the vehicle's behavior to the profile to determine if it is abnormal, thereby avoiding false warnings for vehicles with unique driving characteristics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If a single behavior profile is used for all vehicle types, then the system is simple to operate, but false warnings are generated for vehicles with unique driving characteristics
Solution Approach 1:
The patent segments the monitoring system by creating separate behavior profiles for different vehicle types (e.g., cars, trucks, buses). Each profile contains vehicle-type-specific normal behavior patterns, allowing the system to accurately assess behavior relative to the particular vehicle being monitored rather than applying a universal standard that causes false warnings.
Solution Approach 2:
The system changes the parameters used to evaluate driving behavior based on vehicle type. For example, stopping distance thresholds, acceleration rates, and lane-changing frequencies are adjusted according to the specific vehicle category, enabling reliable detection without generating false alarms for vehicles with inherently different operational characteristics.
2Measurement precision
If vehicle type classification is implemented, then detection accuracy improves, but system complexity increases
Solution Approach 1:
The system divides the monitoring function into discrete vehicle-type-specific profiles, where each profile encapsulates the normal behavior patterns for a particular vehicle category. This segmentation allows accurate measurement of behavior deviations while managing complexity through modular organization of assessment criteria for each vehicle type.
Solution Approach 2:
Despite having multiple vehicle-type-specific profiles, the system maintains a universal evaluation framework that applies the same fundamental assessment logic across all vehicle types. The core monitoring mechanism remains consistent while adapting parameters based on vehicle category, thereby improving precision without proportionally increasing overall system complexity.
Data Source
AI summary
A method according to some embodiments includes sensing, by a sensor set of the ego vehicle, a remote vehicle to generate sensor data describing driving behavior of the remote vehicle. The method further includes classifying a type of the remote vehicle based on the sensor data. The method further includes retrieving a behavior profile for the type of the remote vehicle that identifies criteria for the type of the remote vehicle and classifications of abnormal behavior and normal behavior based on the criteria. The method further includes comparing the behavior of the remote vehicle to the behavior profile for the type of the remote vehicle. The method further includes determining that the behavior of the remote vehicle is normal based on the comparing.