Vehicle Dynamics Classification for Collision Detection
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Solution Overview
Problem
Existing vehicle dynamics detection systems struggle to accurately differentiate between nominal vehicle behavior and collision or loss of control events, as these events can exhibit similar sensor data characteristics, and often fail to provide effective countermeasures.
Innovation Solution
Implementing a vehicle dynamics classification system that utilizes sensor data, predicted dynamics, and environmental information to classify events and provide assistive information for intelligent vehicle responses, employing neural networks and environmental models to distinguish between nominal and adverse conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional vehicle dynamics detection systems are used, then the system complexity is low, but the measurement precision of collision and loss of control detection deteriorates because nominal vehicle behavior and adverse events exhibit similar sensor data characteristics
Solution Approach 1:
The patent transitions from analyzing single-dimensional sensor data to multi-dimensional vehicle dynamics parameters including longitudinal acceleration, lateral acceleration, yaw rate, and steering angle. This dimensional expansion enables differentiation between nominal behavior and adverse events by examining patterns across multiple simultaneous parameters rather than relying on single-sensor thresholds
Solution Approach 2:
The patent introduces an intermediary classification system that processes raw sensor data through multiple layers of analysis. The system uses intermediate representations such as dynamic thresholds, pattern recognition models, and multi-parameter correlations as mediators between raw sensor inputs and final collision/loss of control determinations, improving precision without requiring direct complex hardware modifications
2Reliability
If simple detection systems are used, then the device complexity is low, but the reliability of collision and loss of control detection deteriorates due to inability to provide effective countermeasures
Solution Approach 1:
The patent implements feedback mechanisms where the classification system continuously monitors vehicle dynamics and adjusts detection thresholds based on operational context. The system provides feedback loops that refine its understanding of nominal versus adverse behavior patterns over time, improving reliability through adaptive learning while maintaining manageable system complexity through software-based solutions
Solution Approach 2:
The patent performs preliminary classification of vehicle dynamics events before final determination is made. By pre-processing sensor data to identify potential adverse patterns and categorize them into probability levels, the system prepares detection results in advance, improving reliability of final determinations while reducing the complexity of real-time decision-making hardware requirements
Data Source
AI summary
Provided are methods, systems, and computer program products for vehicle dynamics classification for collision and loss of control detection. Some methods described also include obtaining sensor data associated with dynamics of a vehicle, wherein the dynamics characterize motion of the vehicle and the vehicle is associated with a dynamics event classification. The methods include obtaining predicted dynamics, wherein the predicted dynamics are based on control signals and feedback on control signals from a previous time instance. Additionally, the methods include determining the dynamics event classification of the vehicle based on the dynamics and the predicted dynamics and controlling operation of the vehicle according to the dynamics event classification.