Autonomous Vehicle ECU Lane Determination Logic
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Solution Overview
Problem
Existing autonomous vehicle systems struggle to accurately recognize lanes for both the subject vehicle and nearby vehicles, limiting the effective use of information from sensors like radar or LiDAR during autonomous driving.
Innovation Solution
An electronic control unit (ECU) is developed with autonomous driving logic that classifies objects as stationary or moving, generates clustering groups, determines the boundary of the entire driving road, and compares lane positions based on width to identify the driving lane of the subject vehicle and nearby vehicles.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If lane recognition is performed using images from an imaging device, then the lane in which the subject vehicle is traveling can be recognized, but lanes other than the current lane cannot be recognized
Solution Approach 1:
The patent segments the road recognition task into two parts: using imaging devices to recognize the current lane and using distance measurement sensors to detect nearby vehicles. By dividing the recognition scope into current lane (visual) and surrounding lanes (sensor-based), the system achieves both precise current lane recognition and extended multi-lane awareness.
Solution Approach 2:
The patent makes the distance measurement sensor serve multiple functions: it not only detects nearby vehicles but also determines their lane positions. This multi-functional use of the sensor enables recognition of multiple lanes simultaneously, resolving the limitation of single-lane recognition.
2Device complexity
If only the current lane is recognized using imaging devices, then image processing can be kept simple, but information from distance measurement sensors cannot be effectively utilized
Solution Approach 1:
The patent enables the distance measurement sensor to perform dual functions: vehicle detection and lane determination. By making the sensor universal, the system fully utilizes the collected distance information to recognize both nearby vehicles and their lane positions, eliminating information loss while maintaining reasonable system complexity.
Solution Approach 2:
The patent merges the functions of vehicle detection and lane recognition into a unified process. By combining image data from the imaging device with distance data from the sensor, the system simultaneously determines current lane position and nearby vehicle lanes, reducing overall system complexity while maximizing information utilization.
3Adaptability or versatility
If multiple lanes are to be recognized, then a separate module would be required, but the patent achieves this without additional hardware
Solution Approach 1:
The patent makes existing sensors multi-functional so that no additional hardware modules are needed. The distance measurement sensor simultaneously performs vehicle detection and lane determination, while the imaging device provides both current lane recognition and visual context for nearby vehicles. This universality achieves multi-lane recognition without increasing device complexity.
Solution Approach 2:
The patent merges lane recognition functionality into the existing sensor processing pipelines. By combining data from imaging devices and distance measurement sensors in the control unit, the system achieves multi-lane recognition using the same hardware that would otherwise be used for basic vehicle detection and current lane identification.
Data Source
AI summary
A method of determining a driving lane of an autonomous vehicle is provided. The method includes classifying, by an autonomous driving logic of an electronic control unit (ECU), at least one object sensed in front of the autonomous vehicle as a stationary object or a moving object. A clustering group is generated by clustering the stationary object and a boundary of an entire driving road is determined based on a position of a moving object approaching the subject vehicle among the moving objects and the clustering group. Additionally, the method includes comparing positions of a plurality of lanes based on lane widths of the lanes for travel of the subject vehicle with the boundary of the entire driving road and determining a driving lane of the subject vehicle.