Multi-Sensor Obstacle Mapping for Low-Light Driving Evasion
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Solution Overview
Problem
Existing electronic apparatuses face challenges in efficiently recognizing and navigating around obstacles, particularly in low illumination conditions, leading to prolonged obstacle recognition times and reduced recognition accuracy.
Innovation Solution
An electronic apparatus equipped with a first sensor (such as LiDAR or 3D depth cameras) and a second image sensor, which acquires and processes sensing data to identify driving locations and photographed images, registers events preventing driving by determining a second time point prior to the event, and uses this information to evade obstacles by adjusting its path based on similarity thresholds.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If an electronic apparatus uses image sensors to recognize obstacles during driving, then it can detect obstacles visually, but the recognition time becomes prolonged and recognition accuracy deteriorates in low illumination conditions
Solution Approach 1:
The electronic apparatus performs preliminary mapping of the driving environment using sensors before actual driving begins. This pre-acquired spatial information is stored and used during driving to quickly identify obstacles without requiring real-time image processing, thus reducing recognition time while maintaining accuracy even in low illumination conditions.
Solution Approach 2:
The apparatus divides the driving environment into multiple locations and uses separate sensors (first sensor for spatial mapping, second sensor for imaging) to handle different aspects of obstacle detection. This segmentation allows parallel processing of spatial and visual information, reducing overall recognition time while improving accuracy through multi-sensor fusion.
2Reliability
If an electronic apparatus relies on real-time image processing during driving, then it can identify current obstacles, but the system complexity increases and processing speed decreases
Solution Approach 1:
The patent extracts the complex real-time image processing function from the driving control system and replaces it with pre-acquired spatial information from the first sensor. Only simple comparison operations are performed during driving to match current positions with pre-mapped locations, significantly reducing system complexity while maintaining detection reliability.
Solution Approach 2:
The first sensor acts as an intermediary that pre-processes environmental information into a simplified spatial map. This intermediary representation eliminates the need for complex real-time image analysis during driving, reducing processing system complexity while maintaining reliable obstacle detection through the pre-established spatial model.
3Measurement precision
If an electronic apparatus uses multiple sensors to improve obstacle recognition, then recognition accuracy improves, but the device complexity and data processing burden increase
Solution Approach 1:
The patent merges the functions of multiple sensors into a unified pre-mapping process. The first sensor creates a comprehensive spatial model that integrates environmental information, which is then used by the driving control unit. This merging approach maintains high recognition precision while reducing operational complexity during actual driving by consolidating sensor processing into a single pre-computed spatial representation.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
The solution enables the electronic apparatus to quickly and accurately recognize obstacles, even in low illumination, and adapt its path to avoid them, improving driving stability and efficiency.
Implementation Method 1
a first sensor, a second sensor... wherein the at least one processor, executing the at least one instruction, configured to acquire sensing data through the first sensor
Implementation Method 2
The first sensor may include one of a LiDAR sensor, an infra-red sensor, a three-dimensional (3D) depth camera
Implementation Method 3
the second sensor may include an image sensor configured to acquire photographic images
Data Source
AI summary
An electronic apparatus includes a first sensor, a second sensor, at least one memory storing at least one instruction, and at least one processor operably connected with the first sensor, the second sensor, and the at least one memory, wherein the at least one processor, executing the at least one instruction, configured to acquire sensing data through the first sensor, identify a plurality of driving locations based on the sensing data, acquire a plurality of photographed images through the second sensor, store the plurality of driving locations and the plurality of photographed images in the at least one memory, and based on identifying an event preventing driving, identify a first time point corresponding to the event preventing driving, identify a second time point preceding the first time point by a threshold time, identify a driving location, among the plurality of driving locations, corresponding to the second time point, identify a photographed image, among the plurality of photographed images, corresponding to the second time point, and register the event preventing driving based on event information, and wherein the event information may include the driving location corresponding to the second time point and the photographed image corresponding to the second time point.