Self-driving vehicle collision management with occupant detection
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Solution Overview
Problem
Self-driving vehicles lack effective real-time collision management systems that prioritize occupant safety and adapt to varying collision scenarios, including situations with or without passengers and different types of objects.
Innovation Solution
A system utilizing on-board processors to detect imminent collisions, determine occupant types, and identify objects, generating real-time amelioration actions based on confidence levels and occupant types to minimize harm, including aggressive braking, self-destruction, or crumple zone deployment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a self-driving vehicle implements a real-time collision management system that adapts responses based on occupant detection and collision scenario analysis, then occupant safety is improved, but the system complexity and computational requirements increase
Solution Approach 1:
The system performs preliminary detection of occupants and classification of collision scenarios before the actual collision occurs. Sensors continuously monitor the vehicle interior to identify occupant presence and characteristics, while simultaneously tracking external environment data. This advance preparation allows the system to pre-calculate appropriate response strategies, reducing real-time decision complexity during critical moments.
Solution Approach 2:
The collision management system is divided into distinct functional modules: occupant detection subsystem, collision scenario classification subsystem, response strategy generation subsystem, and execution subsystem. Each module handles specific tasks independently, allowing parallel processing and reducing overall system complexity. The segmentation enables modular design where each component can be optimized and tested separately.
2Measurement precision
If the system performs real-time detection of imminent collisions with high confidence levels and identifies occupant types, then collision response accuracy is improved, but the processing time and computational load increase
Solution Approach 1:
The system implements a tiered detection approach where it continuously monitors basic collision parameters at low computational cost, and only activates full-depth occupant identification and scenario classification when collision risk exceeds predefined thresholds. This partial action strategy maintains high detection accuracy for critical events while minimizing unnecessary computational processing during normal operation.
Solution Approach 2:
When an imminent collision is detected, the system skips non-critical processing steps and focuses computational resources on essential functions: confirming occupant presence, identifying basic occupant characteristics, and selecting pre-prepared response strategies. The system rushes through the decision-making process by utilizing pre-calculated response options rather than generating new strategies in real-time.
3Object-affected harmful factors
If the system generates and implements real-time amelioration actions based on multiple confidence levels and occupant types, then collision harm is minimized, but the device complexity and control mechanisms increase
Solution Approach 1:
The system adjusts vehicle control parameters dynamically based on detected occupant types and collision scenarios. Instead of implementing entirely different control mechanisms for each scenario, the system modifies existing control parameters (braking force, steering angle, acceleration) to achieve appropriate response. This approach minimizes hardware complexity while maintaining the ability to adapt to various collision situations through software-based parameter adjustment.
Solution Approach 2:
The system continuously monitors sensor data during the amelioration action execution and adjusts control commands in real-time based on feedback. Occupant detection systems provide continuous feedback on occupant position and state, allowing the system to refine its response strategy during the collision event. This feedback mechanism enables simplified control logic to achieve complex collision mitigation outcomes through iterative adjustment.
Data Source
AI summary
A method generates and implements a real-time amelioration action for ameliorating an imminent collision between a self-driving vehicle (SDV) and an object. One or more processors detect that an imminent collision by a self-driving vehicle (SDV) is imminent with a confidence C1, and determine whether the SDV has an occupant of occupant type P with a confidence C2. One or more processors identify an object with which the imminent collision by the SDV is imminent with a confidence C3, and then generate and implement, based on C1, C2, C3, and P, a real-time amelioration action for ameliorating the imminent collision between the SDV and the object.