Autonomous Vehicle Threat Vector Prediction via Cloud Intermediary
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Solution Overview
Problem
Conventional autonomous driving systems lack the capability to cooperatively quantify and mitigate potential risks from objects, such as persons and pets, that may pose dangers to vehicles, especially when these objects are not immediately threatening but could become hazards in the future, due to limitations in predicting future risk vectors.
Innovation Solution
The system enables multiple autonomous vehicles to share threat information detected by LIDAR and other sensors, using machine learning and AI to predict the movement of objects and construct a collaborative threat vector model, which includes primary and secondary attributes like speed, direction, and probability, allowing for proactive risk assessment and mitigation across a network of vehicles.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If autonomous vehicles use conventional isolated detection systems, then each vehicle can identify immediate threats within its own sensor range, but the system cannot predict future risk vectors from objects that are currently distant or not yet threatening
Solution Approach 1:
The system performs preliminary action by predicting future threat vectors before objects become immediate threats. Machine learning models analyze current object attributes (position, speed, direction) to forecast future risk levels, enabling vehicles to prepare preventive measures in advance rather than reacting only to imminent dangers.
Solution Approach 2:
A cloud-based machine learning server acts as an intermediary between multiple vehicles. The server receives sensor data from various vehicles, processes it through predictive models, and returns refined threat vector information. This intermediary enables information sharing and collaborative prediction across the vehicle fleet, overcoming individual sensor limitations.
2Reliability
If multiple vehicles share threat information cooperatively, then the system can construct evolving object threat vector models to mitigate potential accidents, but the computational complexity and data processing requirements increase significantly
Solution Approach 1:
The cloud-based server serves as a centralized intermediary that handles the complex computational tasks of aggregating and processing threat data from multiple vehicles. Individual vehicles transmit raw sensor data without performing complex collaborative computations, thereby reducing on-vehicle system complexity while enabling sophisticated group-level threat analysis.
Solution Approach 2:
The system segments computational responsibilities between edge devices (vehicles) and cloud infrastructure. Vehicles perform local sensor data collection and basic processing, while the cloud server handles complex machine learning model execution and cross-vehicle data synthesis. This segmentation distributes computational burden and reduces individual device complexity requirements.
3Speed
If vehicles react only to immediate threats within their current field of view, then the system response is simple and fast, but the system cannot anticipate threats from objects that will enter the vehicle's path in the future
Solution Approach 1:
The system performs preliminary threat identification by predicting future positions and risk levels of detected objects. Machine learning models forecast which currently distant or stationary objects will become threats based on their motion vectors, allowing the vehicle to prepare response strategies in advance while maintaining fast reaction times when threats materialize.
Solution Approach 2:
The threat assessment system is dynamic and continuously updates predictions as objects move and environmental conditions change. Rather than static threat catalogs, the system recalculates threat vectors in real-time based on updated sensor data, enabling both anticipatory identification of future threats and rapid adaptation when situations evolve.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach enhances the ability of autonomous vehicles to anticipate and react to potential threats by sharing predictive threat vectors, enabling more informed decision-making and safer navigation, even when the threat is not immediately imminent, thereby reducing the risk of accidents.
Implementation Method 1
a detector module coupled to the communication module and configured to detect the moving object and to provide one or more secondary attributes associated with the moving object
Data Source
AI summary
The disclosure generally relates to autonomous or semi-autonomous driving vehicles. An exemplary embodiment of the disclosure relates to a system to provide one or more threat vectors to a cluster of vehicles. An exemplary vehicle detection system includes a communication module configured to receive a first threat vector from a first vehicle in a cluster of vehicles. The first threat vector may include a plurality of primary attributes associated with a moving object. The vehicle detection system may also include a detector module configured to detect the moving object and to provide one or more secondary attributes associated with the moving object; and a controller to construct a second threat vector as a function of one or more of the first threat vector, the primary attributes and the secondary attributes associated with the moving object.