Radar-Lidar Wheel Tracking for Close-Range Collision Detection
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Solution Overview
Problem
Existing collision detection systems for motor vehicles struggle to accurately track distant target vehicles and determine collision-relevant data, especially in close proximity, due to the dynamic nature of vehicle movements and the limitations of single-sensor systems.
Innovation Solution
The use of a collision detection device equipped with a fusion of radar and lidar sensors, where the radar sensor identifies wheels of the target vehicle by analyzing Doppler speed variances within a two-dimensional window, and the lidar sensor provides additional data for precise tracking and data determination.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a single radar sensor is used to detect target vehicles, then the device complexity is low, but the measurement precision of collision-relevant data deteriorates at close range
Solution Approach 1:
The patent combines radar sensor data and lidar sensor data through a fusion process to track the target vehicle. The control unit merges information from both sensors, including wheel detection data from radar and geometric feature data from lidar, to achieve precise tracking at close range where single-sensor systems fail.
Solution Approach 2:
The control unit performs multiple functions: it processes radar data for wheel detection and velocity measurement, processes lidar data for geometric feature identification, fuses both data types, and determines collision-relevant parameters. This multi-functional approach enables precise tracking using a single control unit without requiring separate dedicated systems for each sensor type.
2Reliability
If the target vehicle is tracked at close proximity (less than 2 meters), then the relevance for pre-crash detection is improved, but the tracking reliability deteriorates due to dynamic vehicle movements
Solution Approach 1:
The patent merges wheel detection data from the radar sensor with geometric feature data from the lidar sensor to create a more reliable tracking model. This fusion compensates for the limitations of each individual sensor when tracking dynamically moving vehicles at close proximity, maintaining both reliability and data accuracy.
Solution Approach 2:
The system determines multiple collision-relevant parameters including relative velocity, distance, and orientation angles (azimuth and elevation). By monitoring changes in these parameters over time and comparing them against threshold values, the system can reliably detect imminent collisions even when the target vehicle is moving dynamically at close range.
3Measurement precision
If radar sensor data is used alone to determine wheel position and velocity, then the ease of operation is maintained, but the measurement precision of collision-relevant data deteriorates
Solution Approach 1:
The control unit combines wheel detection data from the radar sensor with geometric feature data from the lidar sensor to precisely determine wheel position and velocity. This fusion improves measurement precision while the automated processing maintains ease of operation.
Solution Approach 2:
The system automatically processes and fuses data from both sensors without requiring manual intervention. The control unit autonomously performs wheel detection, geometric feature identification, data fusion, and collision risk assessment, maintaining ease of operation while achieving high precision through multi-sensor integration.
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 enables precise tracking and accurate determination of collision-relevant data, such as speed and position, even in close proximity to the target vehicle, thereby enhancing the effectiveness of pre-crash safety systems.
Implementation Method 1
Environmental detection using a radar sensor, for example, is based on the emission of focused electromagnetic waves and their reflection, particularly by structures along the road or other vehicles.
Implementation Method 2
radar reflection points are derived from the radar sensor data, which determine a position determined by a distance and an azimuth angle and a Doppler velocity of points on the target vehicle
Implementation Method 3
The use of a collision detection device equipped with a fusion of radar and lidar sensors
Data Source
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AI summary
The invention relates to a method (100) and a collision detection device on a motor vehicle (1) for tracking a remote target vehicle (2) for the detection of an impending collision by merging radar sensor data from a first environment sensor (5) designed as a radar sensor with sensor data from a second environment sensor (6), wherein, in one step (101), initially radar sensor data comprising radar reflection points and sensor data of the target vehicle is provided. In a following step (102), a wheel (4a, 4b, 4c, 4d) of the target vehicle (2) is identified from the radar reflection points, with a uniformly sized two-dimensional window with an extent in the distance dimension and in the azimuth dimension being laid around each radar reflection point and a sum of the variances of the Doppler velocities of all radar reflection points contained in the window being determined and assigned to the corresponding radar reflection point and 15 wherein a radar reflection point is determined as a point on a wheel (4a, 4b, 4c, 4d), the total assigned to which is greater than a predefined threshold value. In a subsequent step (103), first radar acquisition data based on the wheel (4a, 4b, 4c, 4d) identified from the radar reflection points is provided. In further steps (104, 105), a wheel (4a, 4b, 4c, 4d) is identified from the sensor data of the second environment sensor (6) and second radar acquisition data (12) is provided based on this. In further subsequent steps (106, 107), the first radar acquisition data and the second radar acquisition data are merged and a parameter of the target vehicle (2) is determined based on this.