Braking Event Classification via Physiological and LiDAR Data
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Solution Overview
Problem
Current methodologies fail to effectively distinguish between different types of hard braking events in driving, such as reactive and intended braking, and do not adequately analyze road features to determine the cause of these events, which is crucial for advanced driver assistance systems (ADAS) and accident prevention.
Innovation Solution
The system analyzes data from vehicle sensors and wearable devices to identify physiological signals, using a Lasso regression model and classification framework to differentiate between reactive and intended hard braking events by extracting features from a Lidar depth scanner, correlating these signals with road environment data to predict and classify braking behaviors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If current methodologies are used to analyze braking events, then general driving behavior can be monitored, but the system cannot distinguish between different types of hard braking events (reactive vs. intended)
Solution Approach 1:
The patent segments hard braking events into distinct categories (reactive vs. intended) by analyzing multiple data dimensions including physiological signals, vehicle dynamics, and environmental factors. This segmentation enables precise classification while maintaining systematic organization of the complex analysis process through modular feature extraction and independent evaluation of each data source
Solution Approach 2:
The patent utilizes changes in multiple parameters simultaneously - physiological parameters (heart rate, galvanic skin response), vehicle parameters (deceleration rate, brake pressure), and environmental parameters (distance to objects, traffic conditions) - to distinguish between reactive and intended braking events. This multi-parameter approach enables accurate differentiation without requiring a single complex indicator
2Loss of information
If physiological signals from wearable devices are analyzed, then driver awareness can be assessed, but the system cannot determine whether signal changes are caused by unexpected traffic conditions or other factors
Solution Approach 1:
The patent merges multiple data sources including physiological signals from wearable devices, vehicle sensor data, and environmental information from LiDAR/cameras into a unified analysis framework. This combination allows the system to correlate physiological changes with specific external stimuli, determining whether signal changes are caused by unexpected traffic conditions or other factors through cross-validation of data streams
Solution Approach 2:
The patent implements feedback loops where physiological signals are continuously monitored and compared against environmental contexts. The system provides feedback by comparing expected physiological responses to actual measurements, allowing it to determine whether observed signal changes are appropriate reactions to traffic conditions or indicate other underlying causes requiring different interpretation
3Difficulty of detecting and measuring
If road environment features are extracted using LiDAR, then unexpected objects can be detected, but the system cannot correlate these features with driver physiological responses to determine causal relationships
Solution Approach 1:
The patent uses physiological signals as an intermediary that connects environmental features detected by LiDAR with driver cognitive states. The physiological data serves as a mediator that translates external road conditions into measurable internal responses, allowing the system to infer causal relationships between unexpected objects and driver reactions through the physiological response pathway
Solution Approach 2:
The patent performs preliminary extraction and classification of road environment features using LiDAR and camera data before analyzing physiological responses. By pre-processing environmental data to identify potential hazards and unexpected objects, the system can then correlate these pre-identified features with subsequent physiological changes, establishing causal relationships through temporal and contextual alignment
Data Source
AI summary
Systems and methods for analyzing a driver's brake behavior are provided. In some aspects, the system includes a memory that stores instructions for executing processes for analyzing the driver's brake behavior, and a processor configured to execute the instructions. In aspects, the processes include: receiving data from one or more multi-modal sensors, determining whether each braking event of a plurality of braking events is a hard braking event; analyzing the one or more physiological signals of the driver to determine whether each of the hard braking events is a reactive hard braking event or an intended hard braking event; applying a Lasso regression analysis and a classification framework for each of the hard braking events to identify features that indicate whether a respective hard braking event is a reactive hard braking event or an intended hard braking event; and generating a signal notifying a vehicle of the features.