Off-road dump truck obstacle discrimination using dynamic radar thresholds
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Solution Overview
Problem
On unpaved off-road surfaces with bumps and potholes, existing obstacle detection systems, such as those using millimeter wave radar, face challenges in accurately distinguishing between vehicles and the road surface due to similar reflection intensities, leading to misrecognition and reduced accuracy.
Innovation Solution
The implementation of a millimeter wave radar and laser radar system integrated with a sensor fusion architecture, including a travel state determination section, distance filter section, and reflection intensity filter section, which calculates cumulative and moving averages of reflection intensities to differentiate between vehicles and non-vehicle obstacles like road surfaces.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If obstacle detection is performed using reflection intensity on unpaved off-road, then detection capability is maintained, but misrecognition of road surface as vehicle increases
Solution Approach 1:
The system dynamically adjusts the reflection intensity threshold based on vehicle acceleration and deceleration states. When the vehicle accelerates or decelerates significantly, the threshold is adjusted to account for changes in reflection intensity caused by vehicle motion, preventing misrecognition of road surface as vehicle while maintaining obstacle detection capability.
Solution Approach 2:
The system changes the threshold parameter for reflection intensity determination based on vehicle operational state. By monitoring acceleration and deceleration, the system adapts the threshold value to distinguish between reflection intensity changes caused by vehicle motion versus those caused by actual obstacles, thereby improving vehicle discrimination reliability on unpaved roads.
2Ease of operation
If millimeter wave radar is mounted on vehicle body, then obstacle detection is enabled, but radar shakes violently on bumpy roads reducing detection accuracy
Solution Approach 1:
The system uses acceleration and deceleration detection as feedback to adjust the reflection intensity threshold. By continuously monitoring vehicle motion state and feeding this information back to the threshold adjustment mechanism, the system compensates for radar shaking effects and maintains detection accuracy on bumpy off-road surfaces.
Solution Approach 2:
The threshold determination mechanism is made dynamic by linking it to real-time vehicle acceleration and deceleration data. This allows the system to adapt to changing vibration conditions caused by bumpy roads, maintaining operational functionality while preserving measurement precision despite radar shaking.
3Reliability
If reflection intensity threshold is set to distinguish vehicle from road surface, then vehicle identification is improved, but misclassification occurs on bumpy surfaces
Solution Approach 1:
The system changes the threshold parameter dynamically based on vehicle acceleration and deceleration states. Instead of using a fixed threshold, the threshold is adjusted according to real-time motion data, allowing the system to maintain reliable vehicle-road surface distinction even when reflection intensity measurements are affected by bumpy road conditions.
Solution Approach 2:
The threshold determination is transformed from a static process to a dynamic one that responds to vehicle motion. By continuously adapting the threshold based on acceleration and deceleration feedback, the system maintains high reliability in vehicle identification while compensating for measurement inaccuracies caused by road surface irregularities.
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 approach effectively suppresses misrecognition of non-vehicles as vehicles, enhancing the accuracy of obstacle detection and reducing unnecessary collision avoidance actions on uneven off-road surfaces.
Implementation Method 1
when a mining dump truck travels on the bumpy road surface, the truck body violently jolts in up-and-down and left-and-right directions, and following these jolts, an obstacle detection device, such as a millimeter wave radar, mounted on the vehicle body also considerably shakes
Implementation Method 2
a millimeter wave or laser light is radiated against a preceding vehicle from an oblique direction or lateral direction instead of squarely opposing the preceding vehicle, and the reflection intensity may become lower
Implementation Method 3
a millimeter wave or laser light is radiated against a preceding vehicle from an oblique direction or lateral direction instead of squarely opposing the preceding vehicle
Data Source
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AI summary
An off-road dump truck includes a vehicle body, a peripheral recognition device, and an obstacle discrimination device. The peripheral recognition device detects obstacle candidates in front of the vehicle body. The obstacle discrimination device classifies the obstacle candidate, which have been detected by the peripheral recognition device, into obstacles and non-obstacles and outputs, as obstacles, the obstacle candidates classified as the obstacles. The obstacle discrimination device includes a travel state determination section, a distance filter section, and a reflection intensity filter section. The travel state determination section determines whether each obstacle candidate is a moving object or a stationary object. The distance filter section compares a distance, where the stationary object was first detected, with a distance threshold. The reflection intensity filter section calculates statistical information based on reflection intensity information on the obstacle candidate, and based on a comparison result of the statistical information with a threshold, classifies the obstacle candidate.