Uncertainty-Aware Decision Trees for Autonomous Vehicle Object Classification
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Autonomous vehicles face challenges in accurately classifying objects due to noise and uncertainty in sensor measurements, leading to potential misclassification and unsafe vehicle control decisions.
Innovation Solution
The implementation of a decision tree that considers uncertainty in feature values by determining probabilities at decision nodes, allowing for multiple paths through the tree and incorporating variance information to provide more robust classification and control instructions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional classification methods are used without considering uncertainty, then the classification process is simple and fast, but the accuracy and reliability of object classification deteriorates due to noise in sensor measurements
Solution Approach 1:
The patent transforms the static classification process into a dynamic probabilistic evaluation. Instead of a single deterministic classification path, the system dynamically evaluates multiple possible classification paths through the decision tree, with each path weighted by its probability. This allows the system to adapt to uncertain sensor measurements by considering multiple outcomes simultaneously, thereby improving reliability without excessive complexity
Solution Approach 2:
The patent changes the parameter representation from deterministic values to probabilistic distributions. By representing sensor measurements as probability distributions rather than fixed values, the system can naturally incorporate measurement uncertainty into the classification process. This parameter transformation enables more reliable classification by accounting for the inherent noise in sensor data
2Measurement precision
If multiple paths through the decision tree are evaluated with probability calculations, then the accuracy of classification improves, but the computational complexity and processing time increases
Solution Approach 1:
The patent performs preliminary calculations of probability distributions during the decision tree traversal process itself, rather than requiring separate post-processing steps. By computing probabilities on-the-fly as the system moves through the tree, it integrates precision improvement with efficient processing, avoiding additional time losses that would result from separate analysis phases
Solution Approach 2:
The patent maintains continuous probabilistic evaluation throughout the entire decision tree traversal, rather than switching between deterministic and probabilistic modes. This continuous approach ensures that precision is maintained at every stage of classification while avoiding the computational overhead of repeatedly switching between different processing modes, thus reducing processing time
3Reliability
If variance information is incorporated into the decision tree, then the robustness of classification improves, but the complexity of data processing and analysis increases
Solution Approach 1:
The patent introduces probability distributions as an intermediary layer between raw sensor measurements and final classification decisions. This intermediary representation simplifies the processing of variance information by providing a standardized mathematical framework that naturally handles uncertainty. Instead of directly processing complex variance data, the system uses probability distributions to mediate the transformation, making the process more manageable and less difficult
Data Source
AI summary
The present disclosure is directed to autonomous vehicle having a vehicle control system. The vehicle control system includes a processing system that receives input values that indicate attributes of an object within a threshold distance of the autonomous vehicle and variance values indicating uncertainty associated with the input values. The processing system also provides a plurality of outcomes that are associated with combinations of split decisions. A given split decision indicates whether a particular input value is above or below a threshold value associated with the given split decision. The processing system further determines (i) a probability that the particular input value is above a threshold value and (ii) a probability that the particular input is below the threshold value for a given split decision. Additionally, the processing system determines one or more likelihoods associated with a given outcome. Further, the processing system provides instructions to control the autonomous vehicle.