School Bus State Estimation for Autonomous Vehicle Response
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Solution Overview
Problem
Autonomous vehicles face challenges in accurately determining the state of school transportation vehicles, such as actively loading or unloading, which affects their motion planning and adherence to traffic regulations.
Innovation Solution
The system analyzes sensor data to estimate the state of a school transportation vehicle from candidate states (actively loading/unloading, imminently loading/unloading, and inactive) and adjusts the autonomous vehicle's motion accordingly, using indicators like flashing lights, door status, and calendar information to compute a probability mass function for decision-making.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the autonomous vehicle uses basic sensor detection to identify school transportation vehicles, then the detection speed is fast, but the accuracy of determining the vehicle state (loading/unloading status) is insufficient
Solution Approach 1:
The patent segments the state estimation process into multiple independent indicator analyses (flashing lights, door status, calendar information, sensor data) that are processed separately and then integrated through a probability mass function. This segmentation allows each indicator to be evaluated independently, improving overall state estimation accuracy while maintaining manageable system complexity through modular processing.
Solution Approach 2:
The patent transitions from traditional binary detection (present/absent) to a multi-dimensional probability-based assessment system. By evaluating multiple indicators across different dimensions (visual, temporal, spatial) and combining them into a probability mass function, the system achieves more nuanced and accurate state estimation without proportionally increasing complexity.
2Reliability
If the autonomous vehicle responds immediately to detected school vehicles, then the response time is short, but the compliance with traffic regulations may be compromised due to inaccurate state determination
Solution Approach 1:
The patent performs preliminary analysis of multiple indicators (flashing lights, door status, calendar data) before making the final state determination. By pre-evaluating these indicators and computing the probability mass function in advance, the system ensures accurate state assessment before executing the response action, thereby maintaining both compliance reliability and timely response.
Solution Approach 2:
The system continuously monitors multiple indicators and updates the probability mass function based on changing conditions. This feedback mechanism allows the autonomous vehicle to adjust its response dynamically, ensuring compliance with traffic regulations while minimizing unnecessary delays by responding as soon as the state probability exceeds the threshold.
3Measurement precision
If the system analyzes multiple indicators to determine school vehicle state, then the state estimation accuracy improves, but the computational complexity increases
Solution Approach 1:
The patent transforms multiple qualitative indicators (flashing lights detected, door status, calendar information) into quantitative parameters that can be processed mathematically. By converting these indicators into a standardized format and applying the probability mass function formula, the system achieves accurate state estimation through systematic parameter transformation rather than complex qualitative analysis.
4Reliability
If the autonomous vehicle slows or stops for every detected school transportation vehicle, then safety is maximized, but the travel efficiency decreases
Solution Approach 1:
The patent applies partial action by only responding (slowing or stopping) when the computed probability that the school vehicle is actively loading or unloading exceeds a predetermined threshold. This selective response approach maintains safety compliance by acting only when necessary, while preserving travel efficiency by avoiding unnecessary deceleration or stopping events.
Data Source
AI summary
This document discloses system, method, and computer program product embodiments for anticipating an imminent state of a school transportation vehicle. For example, the method includes receiving sensor data of an environment near an autonomous vehicle. The method further includes, in response to the sensor data including a representation of a school transportation vehicle, analyzing the sensor data to estimate, from a set of candidate states, a current state of the school transportation vehicle, wherein the candidate states include an actively loading or unloading state, an imminently loading or unloading state, and an inactive state. The method further includes, in response to the estimated current state being either the actively loading or unloading state or the imminently loading or unloading state, causing the autonomous vehicle to slow or stop until the school transportation vehicle is in the inactive state.