Camera-Based Car Entry Intent Prediction for Hands-Free Access
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Solution Overview
Problem
Traditional vehicle access systems require physical keys or devices, which are cumbersome and insecure, and do not adapt dynamically to user intentions and movements, especially in autonomous and semi-autonomous vehicles.
Innovation Solution
A vehicle access control system using external cameras and machine learning to predict user intent based on visual cues such as body posture, gaze direction, and proximity, triggering automatic vehicle entry actions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If physical keys or devices are used for vehicle entry, then security is maintained, but ease of operation deteriorates due to cumbersome procedures
Solution Approach 1:
The patent replaces traditional mechanical key-based entry systems with an optical-based vision system using cameras and machine learning algorithms. The system captures images of the user's face, body posture, and movement trajectory, then uses computer vision processing to predict entry intent and automatically control vehicle access, eliminating the need for physical keys or handheld devices.
Solution Approach 2:
The system enables the vehicle to automatically detect and respond to user entry intent without requiring the user to manually present credentials or activate entry mechanisms. The vision system continuously monitors the user's approach, analyzes visual cues such as gaze direction and body orientation, and autonomously determines when to grant access, making the entry process seamless and hands-free.
2Adaptability or versatility
If traditional access systems are used, then device complexity is low, but adaptability to user intentions deteriorates
Solution Approach 1:
The system performs preliminary analysis of visual cues before the user actually attempts entry. The vision system continuously tracks the user's approach trajectory, head orientation, and body posture in advance, predicting entry intent based on these early indicators. This allows the system to prepare for and respond to entry requests more naturally, anticipating user actions rather than merely reacting to them.
Solution Approach 2:
The system dynamically adjusts its analysis and response based on real-time visual data. Rather than using fixed thresholds or rigid detection rules, the machine learning model continuously evaluates multiple varying parameters including user distance, movement speed, gaze direction, and body orientation, adapting its decision-making process to the specific dynamics of each approach scenario.
3Ease of operation
If visual cue analysis is implemented, then ease of operation improves, but reliability may worsen due to potential misprediction
Solution Approach 1:
The system merges multiple independent visual cue analyses into a unified intent prediction framework. Instead of relying on a single indicator such as proximity alone, the system simultaneously analyzes face detection results, body posture estimation, head orientation, and movement trajectory, combining these multiple data streams to make a more robust and reliable determination of entry intent that reduces false positives.
Solution Approach 2:
The system implements feedback mechanisms where the predicted intent and subsequent entry actions are continuously evaluated against actual user behavior. The machine learning model uses this feedback to refine its prediction algorithms over time, improving accuracy by learning from successful and unsuccessful prediction cases, and adjusting its sensitivity thresholds based on observed patterns in user approach behavior.
Data Source
AI summary
A vehicle access control system for predicting a user's intent to enter a vehicle is achieved through an analysis of video data captured by a plurality of external cameras mounted on the vehicle. The plurality of cameras capture the video data as the user approaches the vehicle. The system includes a processor with an intent prediction module. A memory stores instructions which, when executed by the processor, enable the processor to analyze visual cues of the user from the video data, including body posture, gaze direction, and proximity to the vehicle. Furthermore, the processor extracts user data including a trajectory, head orientation, and body orientation of the user from the video data. The intent prediction module, using the user data, predicts the intent of the user to enter the vehicle and triggers a corresponding vehicle entry action.