Vehicle Sensor Range Inference for Adverse-Weather Cruise Control
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Solution Overview
Problem
Existing vehicle control systems fail to appropriately manage vehicle control when the detection range of surrounding situation sensors is limited due to adverse weather conditions, leading to potential inappropriate control actions.
Innovation Solution
A vehicle control device that infers a limit detection distance using a machine learning model based on sensor data from a learning vehicle, allowing for appropriate speed management during adverse weather conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the surrounding situation sensor is used to detect the preceding vehicle, then the vehicle control system can perform adaptive cruise control, but when the detection range is limited due to adverse weather, the control becomes inappropriate
Solution Approach 1:
The system proactively infers the limit detection distance before actual detection failure occurs by analyzing sensor data patterns and environmental conditions. This preliminary assessment allows the control system to adjust speed limits and control parameters in advance, preventing inappropriate control actions when detection range is limited due to adverse weather conditions
Solution Approach 2:
The system introduces an intermediary inference mechanism that bridges the gap between sensor data and control decisions. By inferring the limit detection distance as an intermediate parameter, the system can make more accurate control decisions even when the sensor cannot directly detect the preceding vehicle, thus resolving the contradiction between detection range limitations and control reliability
2Productivity
If the vehicle control system operates without accurate detection distance information, then it can maintain continuous operation, but the control accuracy and safety are compromised
Solution Approach 1:
The system uses its own sensor data to self-infer the limit detection distance without requiring external input or manual calibration. By analyzing patterns in its existing sensor data and environmental conditions, the system autonomously determines the effective detection range, maintaining continuous operation while improving measurement precision through self-assessment
Solution Approach 2:
The system implements a feedback mechanism where sensor data is continuously analyzed to infer the limit detection distance, which then feeds back into the control decision-making process. This closed-loop approach allows the system to maintain continuous operation while dynamically adjusting control parameters based on inferred detection capabilities, thereby improving both continuity and precision
Data Source
AI summary
A vehicle control device infers a limit detection distance which is a maximum value of a distance from a surrounding situation sensor detectable by the surrounding situation sensor, the surrounding situation sensor being mounted on a host vehicle. The processor infers the limit detection distance of the surrounding situation sensor based on sensor data of the surrounding situation sensor by using a machine learning model obtained by performing learning using teacher data which is a data set of sensor data of a learning surrounding situation sensor mounted on a learning vehicle and a label indicating the limit detection distance of the learning surrounding situation sensor when the sensor data of the learning surrounding situation sensor is obtained.