Autonomous Vehicle Sensor Data Retention for Misbehavior Detection
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Solution Overview
Problem
Autonomously driving vehicles face risks from deliberate misbehavior by other road users, which can lead to accidents or disruptions in traffic due to their reactive braking behaviors, and there is a need to identify and document such misbehavior for safety and operational improvement.
Innovation Solution
A method for autonomously driving vehicles to store environment detection data in a permanent memory when trigger criteria are met, analyzing sensor data using AI methods to detect deliberate misbehavior, and transmitting this data for verification and potential use in improving vehicle functionality and reporting to authorities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If an autonomously driving vehicle performs emergency braking to prevent accidents, then safety of the autonomous vehicle is improved, but traffic following behind is put at risk
Solution Approach 1:
The system performs preliminary actions by continuously monitoring the environment and detecting potential threats before they materialize into accidents. The situation analysis and trigger criterion evaluation occur proactively, allowing the vehicle to prepare for emergency maneuvers only when necessary, thereby reducing unnecessary emergency braking that could harm following traffic.
Solution Approach 2:
The system implements feedback by continuously analyzing sensor data, evaluating trigger criteria, and adjusting driving decisions based on the situation analysis results. This feedback loop ensures that emergency braking is performed only when the analysis confirms a genuine threat, preventing false positives that could endanger following traffic while maintaining high safety standards.
2Measurement precision
If the autonomously driving vehicle continuously monitors and analyzes sensor data to detect deliberate misbehavior, then identification accuracy of misbehavior is improved, but computational resources and processing time are increased
Solution Approach 1:
Sensor data is continuously recorded and pre-processed in advance, with relevant parameters extracted and stored for later analysis. This preliminary action allows the system to quickly evaluate trigger criteria and perform situation analysis only when necessary, reducing real-time computational burden while maintaining high detection accuracy.
Solution Approach 2:
The monitoring system is segmented into distinct functional modules: data acquisition, trigger criterion evaluation, situation analysis, and misbehavior detection. Each module processes specific aspects of the data independently, allowing parallel processing and optimizing computational resource utilization while maintaining comprehensive monitoring capabilities.
3Loss of information
If the autonomously driving vehicle stores environment detection data in permanent memory when trigger criteria are met, then documentation capability of misbehavior is improved, but memory usage and data management complexity are increased
Solution Approach 1:
The system performs preliminary evaluation of trigger criteria continuously, identifying situations that warrant data storage before they occur. This allows the system to selectively store only relevant data in permanent memory based on pre-defined criteria, avoiding unnecessary storage operations and reducing memory management complexity while ensuring comprehensive documentation of misbehavior incidents.
Solution Approach 2:
Different storage strategies are applied to different types of data based on their importance and characteristics. Critical data related to misbehavior incidents is stored in permanent memory with high retention priority, while routine operational data uses different storage management. This local differentiation optimizes memory usage and simplifies data management by treating different data categories differently.
Data Source
AI summary
A method for an autonomously driving vehicle to store environment detection data in a permanent memory, wherein a situation analysis is started when a trigger criterion is fulfilled; the situation analysis carries out an evaluation of data from at least one environment detection sensor with regard to misbehavior directed towards the autonomous vehicle; and depending on the result of the situation analysis, data from at least one environment detection sensor is stored in a permanent memory.