Object Action Prediction Using Kinematic-Semantic Fusion
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Solution Overview
Problem
Current autonomous driving systems face challenges in accurately predicting the future actions of objects, such as vehicles or pedestrians, due to limited integration of kinematic and semantic information from various sensors, leading to less precise tracking and decision-making.
Innovation Solution
A method that fuses kinematic object information from vehicle sensors with semantic information from a planner, using an alpha-beta filter like a Kalman filter, to generate a prediction signal for future actions, allowing for more accurate tracking and control of the vehicle.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If only kinematic object information from vehicle sensors is used for prediction, then the system complexity remains low, but the prediction accuracy is insufficient
Solution Approach 1:
The patent combines kinematic object information from vehicle sensors with semantic information from the planner to form a fusion signal. This merging of different information types enables more accurate prediction of future object actions while maintaining a manageable system architecture through standardized fusion processes.
Solution Approach 2:
The system implements feedback by using the fusion signal, which incorporates both sensor data and planner semantic information, to generate more accurate prediction signals. This feedback loop allows the system to continuously improve prediction accuracy by integrating multiple information sources.
2Measurement precision
If multiple sensor signals and semantic information are fused, then the prediction accuracy improves, but the data processing complexity increases
Solution Approach 1:
The fusion signal serves multiple functions: it integrates kinematic information from sensors, incorporates semantic information from the planner, and provides a unified input for generating prediction signals. This multi-functionality reduces the need for separate processing paths and simplifies the overall data flow.
Solution Approach 2:
The fusion signal acts as an intermediary that bridges sensor data and planner semantic information. By creating this intermediate representation, the system simplifies the integration process and makes the data flow more manageable, reducing processing complexity while maintaining prediction accuracy.
3Measurement precision
If semantic information from the planner is integrated into the fusion process, then the prediction of future actions becomes more accurate, but the system architecture becomes more complex
Solution Approach 1:
The system uses feedback by incorporating planner semantic information back into the fusion process. The fusion signal, which includes both sensor kinematic data and planner semantic data, feeds into the prediction generation process, creating a closed-loop system that improves accuracy without requiring fundamental architectural changes.
Solution Approach 2:
The planner generates semantic information about objects and their contexts in advance, which is then integrated into the fusion signal. This preliminary preparation of semantic data allows for more accurate predictions without adding complexity to the real-time processing architecture, as the semantic context is already prepared before the prediction step.
Data Source
AI summary
A method for predicting a future action of an object for a driving assistance system for a highly automated mobile vehicle. At least one sensor signal from at least one vehicle sensor of the vehicle is read in, the sensor signal representing at least one piece of kinematic object information concerning the object that is detected by the vehicle sensor at an instantaneous point in time. A planner signal from a planner of the autonomous driving assistance system is read in, the planner signal representing at least one piece of semantic information concerning the object or the surroundings of the object at a point in time in the past. The kinematic object information is fused with the semantic information to obtain a fusion signal. A prediction signal is determined using the fusion signal, the prediction signal representing the future action of the object.