Adaptive Vehicle Sensor Parameter Control for Surroundings Mapping
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional methods for monitoring vehicle surroundings lack the ability to select optimum parameters deliberately, leading to inefficient detection of objects in various environmental conditions.
Innovation Solution
A method where a vehicle acquires and transmits data to a central device, which selects and sends perception parameters to configure sensors optimally based on location, orientation, and environmental conditions, enabling precise monitoring and filtering of raw data to generate or update a map of the surroundings.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional sensor monitoring methods are used without adaptive parameter selection, then the system structure remains simple, but the detection precision and reliability of object detection deteriorate in varying environmental conditions
Solution Approach 1:
The patent implements dynamic adaptation of sensor parameters (sampling rate, detection threshold, sensitivity) based on real-time environmental conditions such as weather, time of day, and location. The control device continuously adjusts these parameters to optimize detection precision for different scenarios, transforming a static sensor system into a dynamic one that adapts to changing conditions.
Solution Approach 2:
The core mechanism involves changing sensor operating parameters (sampling rate, threshold values, sensitivity settings) according to environmental conditions. The control device receives environmental data and modifies sensor parameters accordingly - for example, increasing sampling rate in adverse weather or adjusting thresholds based on time of day, thereby improving detection precision without requiring hardware changes.
2Reliability
If sensor sensitivity is increased to detect all objects, then detection rate improves, but false detections due to interference increase
Solution Approach 1:
The control device dynamically adjusts the detection threshold parameter based on environmental conditions and interference levels. When interference is detected or environmental conditions suggest high noise levels, the threshold is raised to filter out false detections. When conditions are favorable, the threshold is lowered to improve detection rate, thus balancing reliability and false detection rates.
Solution Approach 2:
The system implements feedback mechanisms where detection results and environmental data are continuously monitored. The control device uses this feedback to adjust sensor parameters in real-time - for example, if false detections increase, the system automatically adjusts thresholds or sampling rates to reduce interference, creating a self-correcting system that maintains optimal detection performance.
3Adaptability or versatility
If sensor parameters are fixed, then device complexity is low, but adaptability to different environmental conditions and locations deteriorates
Solution Approach 1:
The sensor system performs self-adjustment through the control device that automatically modifies parameters based on environmental input. The system serves itself by detecting environmental conditions and autonomously optimizing its own operation without external intervention, thereby achieving high adaptability while keeping the control mechanism relatively simple and automated.
Solution Approach 2:
The control device serves multiple functions: it monitors environmental conditions, processes sensor data, adjusts sensor parameters, and filters detections. This multi-functional approach allows a single device to handle various environmental scenarios and sensor types, achieving universality and adaptability across different conditions without requiring separate specialized systems for each scenario.
Data Source
AI summary
A method for generating a map of the surroundings of a vehicle including at least one sensor. First data are acquired by the vehicle, the first data including a position of the vehicle and information about the type of the sensor. The first data are transmitted to a central device by the vehicle. Perception parameters are selected by the central device in view of the transmitted first data. The selected perception parameters are received by the vehicle, and the sensor is configured, using the selected perception parameters. The surroundings of the vehicle are monitored by the sensor, raw data being recorded, and the raw data being filtered by the vehicle, using the selected perception parameters. Second data are transmitted to the central device by the vehicle, the second data representing the monitored surroundings. A map is generated and/or updated by the central device based on the second data.