Probabilistic Object Detection for Autonomous Driving Safety
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Solution Overview
Problem
Current object detection algorithms in unmanned driving technologies are prone to inaccuracies and safety issues due to complex environments, sensor noise, and limited training sets, leading to unreliable motion decision-making.
Innovation Solution
A method that performs probabilistic object detection on image information from unmanned vehicles, generates environmental state information, and determines vehicle control actions using a hybrid decision-making framework that considers uncertainty, ensuring safety by accounting for potential inaccuracies in perception results.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If probabilistic object detection is used to account for uncertainty, then safety of motion decision-making is improved, but device complexity increases
Solution Approach 1:
The decision-making process is segmented into multiple independent modules: probabilistic object detection module, environmental state information generation module, optional action set generation module, and action selection module. Each module processes specific aspects of the decision-making task, allowing the system to handle uncertainty through structured segmentation rather than monolithic complexity.
Solution Approach 2:
The patent introduces a new dimension of probabilistic reasoning into the traditional object detection and decision-making pipeline. By generating multiple probabilistic detection results with associated confidence levels and creating corresponding environmental state information sets, the system transforms single-deterministic decisions into multi-dimensional probabilistic evaluations, improving safety without overwhelming complexity.
2Reliability
If multiple probabilistic detection results are processed to generate environmental state information sets, then decision-making reliability is improved, but loss of time increases
Solution Approach 1:
The system performs preliminary grouping of probabilistic detection results into environmental state information sets before generating action options. By pre-organizing detection results and their associated uncertainties into structured state representations, the system reduces the computational burden during the action selection phase, thereby minimizing time loss while maintaining high reliability.
Solution Approach 2:
The probabilistic object detection module automatically generates confidence levels and uncertainty measurements for each detection result without requiring external validation. This self-service capability allows the system to efficiently process multiple detection results by leveraging their inherent probabilistic information, reducing the time needed for additional verification while improving decision-making reliability.
Data Source
AI summary
A method, an apparatus, a computer storage medium and a terminal for implementing autonomous driving decision-making are disclosed. Image information is processed by the probabilistic object detection to obtain a probabilistic object detection result set containing multiple probabilistic object detection result. An uncertainty in the object detection process is estimated by the probabilistic object detection results contained in the set of the probabilistic object detection result. An environmental state information set is generated from the probabilistic object detection results in the probabilistic object detection result set and the perceptual information, then an optional action set considering the uncertainty is generated using a preset decision-making method, and an action for vehicle driving control is determined according to the optional action set and the environmental state information set.