Automatic Driving Control Parameters for Non-Average Road Environments
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Solution Overview
Problem
Automatic driving control devices struggle to maintain control in non-average specific road environments, leading to cancellation of automated driving, which limits its daily usability due to inability to adapt to varying road conditions.
Innovation Solution
An automatic driving control device equipped with an external surrounding recognition sensor, a learning recording device, and an automatic driving control quantity calculation unit that calculates and adjusts control parameters based on real-time data and predicted traveling data, integrating relative positions and longitudinal distances to ensure continuous control.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If parameters are adjusted based on average values or precision of feature values across different road environments, then control performance on highways (longest travel distance) is improved, but control performance on other road types (alleys, city roads, industrial areas) deteriorates
Solution Approach 1:
The patent segments the continuous road environment into discrete road types (highway, city road, alley, industrial area road) based on map data and vehicle position. This segmentation allows the system to identify which specific road type the vehicle is currently on and apply appropriate control parameters for each type, rather than using a single averaged parameter set that compromises performance across all environments.
Solution Approach 2:
The patent implements local quality by assigning different control parameters to different road types. The control parameter setting unit selects parameters tailored to the specific road environment (e.g., stricter lane keeping for alleys, more flexible control for highways) based on the current road type identification, ensuring optimal performance for each local condition rather than a uniform approach.
2Reliability
If automatic driving control is optimized for average road conditions, then overall system reliability improves, but control cancellation occurs in non-average specific road environments
Solution Approach 1:
The patent performs preliminary action by pre-storing multiple sets of control parameters corresponding to different road types in the storage unit. Before entering a specific road environment, the system identifies the road type using map data and vehicle position, and pre-selects the appropriate control parameters. This preparation ensures that when the vehicle enters a non-average road environment, suitable parameters are already available, preventing control cancellation and maintaining reliability.
Solution Approach 2:
The patent implements dynamics by making the control parameters adaptive and changeable based on real-time road environment identification. The control parameter setting unit dynamically switches between different parameter sets according to the current road type (highway, city road, alley, etc.), allowing the system to maintain reliability across varying road conditions rather than being fixed to average conditions.
3Ease of operation
If control parameters are set to match driver behavior on all road types, then driver comfort improves, but control complexity increases
Solution Approach 1:
The patent implements self-service by enabling the system to automatically identify the current road type using map data and vehicle position, and autonomously select the appropriate control parameters without driver intervention. The control parameter setting unit performs this selection based on pre-stored parameter sets corresponding to different road types, improving driver comfort through adaptive control while avoiding the complexity of manual parameter adjustment by the driver.
Data Source
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AI summary
Control can be continued so that control of automatic driving is not canceled even in a non-average specific road environment. An automatic driving control device, which controls an actuator of a vehicle in order to automatically travel on a road without driver operation through the use of an external surrounding recognition sensor for recognizing external surroundings and information from the external surrounding recognition sensor, has a learning recording device for recording external surrounding recognition information and vehicle status information when the driver is driving, an output comparison device for comparing the information recorded in the learning recording device with the processing results when the information recorded in the recording device is processed by the automatic driving control device, and a control parameter setting device for setting control parameters so that the results compared by the output comparison device approach the driving of the driver.