Autonomous Vehicle Driver Intent Prediction via Body Language
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Solution Overview
Problem
Autonomous vehicles face challenges in accurately predicting the motion of human-driven vehicles, as they lack direct information about the intentions of human drivers, which is crucial for safe navigation and collision avoidance.
Innovation Solution
Incorporating sensors and computer vision algorithms trained with deep neural networks to estimate driver gaze direction and recognize gestures, correlating this information with expected vehicle motion, and using LIDAR or range sensors to supplement data, allowing autonomous vehicles to interpret driver intent from body language.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If autonomous vehicles use traditional sensors and algorithms to monitor driving positions and velocity, then they can detect imminent crashes and intervene, but they cannot accurately predict the intentions of human drivers in other vehicles
Solution Approach 1:
The patent introduces computer vision algorithms and deep neural networks as intermediary components that process visual information from cameras to infer driver intent. These algorithms act as mediators between the autonomous vehicle's sensors and the human driver's unexpressed intentions, translating visual cues like gaze direction and gestures into predictive information about future vehicle movements.
Solution Approach 2:
The patent replaces traditional mechanical sensing methods with optical sensing and computational analysis. Instead of relying solely on physical sensors to detect vehicle parameters, the system uses camera-based visual perception and neural network processing to detect and interpret human body language, substituting mechanical detection with optical and computational approaches.
2Measurement precision
If autonomous vehicles incorporate computer vision algorithms and deep neural networks to detect driver body language, then they can infer driver intent and predict vehicle motion more accurately, but the system complexity and computational requirements increase
Solution Approach 1:
The patent makes the computer vision system multi-functional by using the same camera and neural network infrastructure for multiple purposes: detecting driver presence, analyzing body language, recognizing gestures, and tracking gaze direction. This universal approach allows one system to perform multiple functions that would otherwise require separate specialized components, thereby managing complexity while maintaining high measurement precision.
3Measurement precision
If autonomous vehicles use LIDAR and range sensors to supplement data, then they can enhance prediction accuracy of human-driven vehicle motion, but the cost and device complexity increase
Solution Approach 1:
The patent merges multiple sensing modalities (cameras, LIDAR, range sensors) into an integrated perception system that processes complementary information streams. By combining visual data from cameras with depth information from LIDAR and range sensors, the system creates a more robust and accurate model of driver intent and vehicle motion, where each sensor type compensates for the limitations of others.
Data Source
AI summary
Systems, methods, and devices for predicting driver intent and future movements of a human driven vehicles are disclosed herein. A computer implemented method includes receiving an image of a proximal vehicle in a region near a vehicle. The method includes determining a region of the image that contains a driver of the proximal vehicle, wherein determining the region comprises determining based on a location of one or more windows of the proximal vehicle. The method includes processing image data only in the region of the image that contains the driver of the proximal vehicle to detect a driver's body language.