Vehicle Lidar Road Profiling for Predictive Surface Estimation
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Solution Overview
Problem
Existing methods for estimating road conditions, such as using IMU, wheel speed sensors, and map service providers, face delays, offsets, and limited accuracy, especially in low visibility situations, and cannot effectively predict road conditions ahead of the vehicle.
Innovation Solution
A vehicle system that uses a 3D sensor, such as a LIDAR sensor, to obtain data points representing longitudinal, lateral, and vertical coordinates of the road, which are then segmented and used to estimate the vertical position of the road, combining this data with the vehicle's motion state to accurately estimate road conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If IMU and wheel speed sensors are used to estimate road conditions, then road condition estimation is provided, but delays and offsets are introduced in the estimation
Solution Approach 1:
The patent replaces mechanical sensors (IMU, wheel speed sensors) with a LIDAR-based optical system to measure road conditions. The LIDAR sensor directly measures the vertical position of the road surface by detecting reflected light, eliminating the need for mechanical motion sensors and their associated delays in estimating road inclination and anomalies.
2Measurement precision
If map service providers are used to provide road condition information, then road condition data is obtained, but availability and accuracy depend on map resolution and features
Solution Approach 1:
The vehicle system performs self-measurement of road conditions using an onboard LIDAR sensor, eliminating dependency on external map service providers. The system directly measures the road surface vertical position and calculates road inclination and anomalies independently, without requiring pre-existing map data or external services.
3Measurement precision
If camera images are used for road damage and anomaly detection, then road condition information is obtained, but accuracy is limited especially in low visibility situations
Solution Approach 1:
The patent replaces passive optical camera imaging with active LIDAR light detection and ranging. The LIDAR system emits light pulses and measures the time of flight and intensity of reflected light to directly determine the vertical position of the road surface, providing accurate measurements independent of ambient visibility conditions unlike camera-based systems.
4Measurement precision
If suspension signals are used to estimate road condition, then current road condition is estimated, but future road conditions ahead of the vehicle cannot be estimated
Solution Approach 1:
The LIDAR sensor is mounted to detect the road surface ahead of the vehicle in the forward driving direction, enabling preliminary measurement of future road conditions before the vehicle reaches them. This allows the system to estimate road inclination, vertical position, and anomalies in advance, providing predictive information for upcoming road sections.
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
This method enables efficient and accurate estimation of road conditions, including geometrical profiles and anomalies, improving driving safety, ride comfort, and energy efficiency by allowing for real-time adjustments to vehicle systems such as suspension, steering, and regenerative braking.
Implementation Method 1
one or more Light Detection and Ranging, LIDAR, sensors
Implementation Method 2
Lidar sensors transmit light pulses at certain known angles and receive the returned light after reflection, thereby being able to calculate the distance to objects in the real world
Data Source
Figure 1~2
Figure 3A~3B
Figure 4~5B
AI summary
A method performed by a vehicle system for enabling estimation of a condition of a road comprising obtaining, from a sensor mounted on a vehicle, first data comprising a first plurality of data points, each comprising longitudinal, lateral and vertical coordinates representing dimensions of a part of the road at a first time, obtaining a second plurality of data points, each comprising longitudinal, lateral and vertical coordinates representing dimensions of the part of the road at a second time, wherein the first time is more recent than the second time, segmenting the first plurality of data points into a plurality of segmented data areas based on longitudinal and lateral coordinates of the first plurality of data points, and estimating a respective vertical position in each segmented data area based on vertical coordinates of the second plurality of data points and a motion state of the vehicle at the second time.