Adaptive Object Tracking During Vehicle Acceleration
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Solution Overview
Problem
Existing object tracking algorithms in vehicles, particularly during acceleration events, suffer from significant errors and loss of lock due to latency and failure to account for sudden motion anomalies, leading to inaccurate position and velocity estimation, which is critical for automated vehicle control.
Innovation Solution
A method that adapts the tracking algorithm by reducing the influence of predictive motion models and historical states during detected acceleration, using a neural network to classify Radar-Doppler maps and adjust the tracking algorithm based on relative acceleration, incorporating current frame data from radar, lidar, or camera systems, and Kalman filters to improve accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Stability of the object's composition
If a predictive motion model and historical state are used for object tracking, then tracking stability is improved, but measurement precision deteriorates during acceleration events
Solution Approach 1:
The tracking algorithm dynamically adjusts the weight between predictive motion model and measured object data based on detected acceleration conditions. During acceleration events, the system transitions from relying on historical predictive models to prioritizing current measured data, making the tracking system adaptive to changing motion conditions rather than static
Solution Approach 2:
The system changes the parameter weights in the tracking algorithm based on acceleration detection. When acceleration is detected, the weight of the predictive motion model is reduced and the weight of current measured object data is increased, thereby adjusting the tracking precision to match the dynamic conditions
2Device complexity
If a standard tracking algorithm is used, then device complexity is minimized, but reliability deteriorates during acceleration events
Solution Approach 1:
The tracking system incorporates dynamic adaptation by detecting acceleration conditions and adjusting algorithm parameters in real-time. This dynamic behavior improves reliability during acceleration events without requiring a complete redesign of the tracking algorithm, maintaining a balance between complexity and performance
Solution Approach 2:
An acceleration detection mechanism serves as an intermediary that monitors motion conditions and triggers appropriate adjustments to the tracking algorithm. This intermediary layer enables the system to respond reliably to acceleration events while keeping the core tracking algorithm relatively simple
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 enhances the accuracy of object tracking during acceleration events by prioritizing instantaneous data over historical and predictive models, reducing errors and maintaining lock on targets, thereby improving automated vehicle control applications without requiring additional hardware.
Implementation Method 1
receiving the Radar-Doppler, RD, map from a radar sensing system of a vehicle, detecting relative acceleration of an object with respect to the vehicle based on the RD map
Data Source
AI summary
Vehicles and methods for tracking an object and controlling a vehicle based on the tracked object. A Radar-Doppler (RD) map is received from the radar sensing system of the vehicle and relative acceleration of an object with respect to the vehicle is detected based on the RD map so as to provide acceleration data. A current frame of detected object data is received from a sensing system of the vehicle. When the relative acceleration has been detected, a tracking algorithm is adapted to reduce the influence of the predictive motion model or the historical state of the object and the object is tracked using the adapted tracking algorithm so as to provide adapted estimated object data based on the object tracking. One or more vehicle actuators are controlled based on the adapted estimated object data.