Radar Back Tracking for Vehicle Platooning
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Solution Overview
Problem
In vehicle platooning systems, accurately identifying and tracking a lead vehicle using radar data is challenging due to ambiguous reflections and changing conditions, which affects the safe and efficient maintenance of a desired gap between vehicles.
Innovation Solution
The system employs techniques such as categorizing radar point candidates based on distance and velocity, using a bounding box to define the expected position of the lead vehicle, and applying clustering algorithms like mean shift to identify the back of the lead vehicle, along with Kalman filtering for state estimation and sensor data fusion from various sources like GPS and wheel speed sensors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If radar systems are used to determine distance between vehicles, then measurement capability is provided, but reliable identification and tracking of the lead vehicle becomes difficult due to ambiguous reflections and changing conditions
Solution Approach 1:
The patent segments the radar detection process into multiple independent mechanisms: (1) radar-based distance measurement, (2) GPS/ GNSS-based position data, and (3) visual recognition systems. Each mechanism independently contributes to vehicle identification and tracking, so that ambiguous radar reflections can be resolved by cross-referencing with position data and visual confirmation, thereby maintaining measurement precision while improving identification reliability
Solution Approach 2:
The patent introduces GPS/ GNSS position data and visual recognition as intermediary verification layers between the radar system and the final vehicle identification decision. These intermediaries provide additional context to disambiguate radar reflections, allowing the system to reliably identify and track the lead vehicle even when radar data alone is ambiguous
2Use of energy by moving object
If vehicles follow closely together to save fuel, then energy efficiency is improved, but safety is compromised when done manually
Solution Approach 1:
The patent implements self-service through autonomous vehicle control systems that automatically maintain optimal following distances without driver intervention. The system continuously monitors radar distance measurements, GPS position data, and visual recognition information to autonomously adjust vehicle speed and position, enabling safe close-following that reduces fuel consumption while eliminating manual control limitations
Solution Approach 2:
The patent employs continuous feedback loops where radar distance measurements, GPS position data, and visual recognition results are constantly fed back to the autonomous control system. This feedback enables real-time adjustments to maintain safe following distances while optimizing fuel efficiency, allowing vehicles to follow closely together safely through automated closed-loop control
3Reliability
If multiple independent mechanisms are used to determine vehicle distance, then identification reliability is improved, but system complexity increases
Solution Approach 1:
The patent implements multi-functionality by designing a unified control system that processes multiple data types (radar measurements, GPS position data, visual recognition information) through a single integrated architecture. This universal system performs both distance measurement and vehicle identification functions simultaneously, reducing overall system complexity compared to separate dedicated systems for each function while maintaining improved identification reliability through multi-mechanism verification
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 reliable identification and tracking of the lead vehicle, ensuring a stable and efficient gap maintenance, even under changing conditions, enhancing safety and fuel efficiency in vehicle platooning applications.
Implementation Method 1
A radar unit on the second vehicle provides a current radar scene including a set of detected object points, each having an indicated position and velocity relative to the second vehicle.
Implementation Method 2
described techniques can be used in conjunction with a variety of different distance measuring technologies including radar, LIDAR, sonar units or any other time-of-flight distance measuring sensors
Data Source
AI summary
A variety of methods, controllers and algorithms are described for identifying the back of a particular vehicle (e.g., a platoon partner) in a set of distance measurement scenes and/or for tracking the back of such a vehicle. The described techniques can be used in conjunction with a variety of different distance measuring technologies including radar, LIDAR, camera based distance measuring units and others. The described approaches are well suited for use in vehicle platooning and/or vehicle convoying systems including tractor-trailer truck platooning applications. In another aspect, technique are described for fusing sensor data obtained from different vehicles for use in the at least partial automatic control of a particular vehicle. The described techniques are well suited for use in conjunction with a variety of different vehicle control applications including platooning, convoying and other connected driving applications including tractor-trailer truck platooning applications.