Autonomous Vehicle Risk Scoring for Falling Object Avoidance
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Solution Overview
Problem
Autonomous vehicles lack the capability to predict and prevent damage from falling objects such as trees or debris, as existing AI systems cannot identify potential risks like falling trees due to environmental changes, leading to inadequate consideration of safety in parking spaces.
Innovation Solution
The system monitors changes in ground vibration patterns, combines this data with historical information on soil, wind, tree age, and tree fall history to predict potential impacts, and proactively moves the vehicle to a safe zone if the risk of damage exceeds a predetermined threshold.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If existing AI systems are used for autonomous vehicle navigation, then basic driving decisions can be made, but the system cannot identify potential risks like falling trees or debris
Solution Approach 1:
The system performs preliminary analysis of environmental factors (tree age, soil conditions, wind patterns) before damage occurs. By proactively assessing risk scores based on historical and real-time data, the system predicts potential falling objects and relocates the vehicle beforehand, rather than reacting after damage happens.
Solution Approach 2:
The patent introduces an intermediary risk assessment layer between the autonomous vehicle and environmental hazards. This layer analyzes multiple data sources (IoT sensors, historical data, environmental conditions) to generate risk scores, acting as a mediator that translates complex environmental factors into actionable safety decisions.
2Reliability
If the vehicle continuously monitors environmental data to predict falling objects, then safety is improved, but computational resources and energy consumption increase
Solution Approach 1:
The system focuses computational resources on locally relevant risk factors rather than continuously analyzing all environmental data. It selectively monitors specific parameters (ground vibration, tree proximity, soil moisture) based on the vehicle's current context and predetermined risk thresholds, reducing overall energy consumption while maintaining high reliability for critical threats.
Solution Approach 2:
The system dynamically adjusts monitoring parameters and risk thresholds based on environmental conditions. When risk levels are low, it reduces monitoring intensity; when risk factors emerge (e.g., high wind speeds, unstable ground), it intensifies data collection and analysis, optimizing energy usage according to actual safety needs.
3Object-affected harmful factors
If the vehicle relocates to safe zones when risks are detected, then damage risk is reduced, but travel time and operational efficiency decrease
Solution Approach 1:
The system applies preliminary anti-action by proactively identifying safe zones and preparing relocation routes before damage occurs. When risk scores exceed thresholds, the vehicle has already identified alternative locations and can execute relocation quickly, minimizing time loss while preventing damage.
Solution Approach 2:
The system applies risk thresholds that trigger relocation only when necessary, avoiding excessive relocations for minor risks. By setting predetermined thresholds for risk scores, it balances safety with operational efficiency, performing partial action (relocation) only when the harm exceeds acceptable levels.
Data Source
AI summary
In an approach to improve improving impact prevention in both manual and autonomous vehicles embodiments of the present invention generate a knowledge corpus based on data collected, from one or more internet of thing (IoT) sensors, associated with one or more predetermined risk factors of one or more predefined risks over time. Further, embodiments receive from the one or more IoT sensors input data to observe a vertical space above a vehicle and identify an object in a predetermined area based on the received input data and the knowledge corpus. Additionally, utilize the knowledge corpus and the input data to generate a risk score for the identified object and responsive to the risk score being above a predetermined threshold, embodiments issue a command for the vehicle to continue to drive or to progress to a predetermined safe zone.