Autonomous Vehicle Risk Processing for Real-Time Hazard Response
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Solution Overview
Problem
Autonomous vehicles face challenges in identifying and responding to risks such as pedestrian crossings, collisions with other vehicles, and adverse driving conditions, as existing systems lack effective methods for real-time risk assessment and adaptive driving adjustments.
Innovation Solution
The system processes sensor signals to identify risks by analyzing object positions, speeds, and driving behaviors, and modifies the autonomous driving capability by executing lane changes or trajectory adjustments, and generating alerts or reports, while integrating prior information and remote operator interventions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If autonomous vehicles use existing safety systems and risk prediction methods, then basic safety functions are provided, but real-time risk assessment and adaptive response capabilities are insufficient
Solution Approach 1:
The system dynamically modifies autonomous driving capabilities in real-time based on identified risks. The autonomous driving capability is modified in response to the risk, allowing the vehicle to adapt its behavior dynamically rather than relying on static safety systems. This enables continuous adjustment of driving parameters to match current risk conditions.
Solution Approach 2:
The system implements a closed-loop feedback mechanism where sensor signals are continuously received, risks are identified based on these signals, and the autonomous driving capability is modified in response. This feedback loop enables real-time risk assessment and adaptive response, improving both reliability and adaptability simultaneously.
2Difficulty of detecting and measuring
If the vehicle system continuously monitors and analyzes sensor signals for real-time risk identification, then risk detection capability is improved, but system complexity and computational load increase
Solution Approach 1:
The system uses a unified risk identification mechanism that processes sensor signals for multiple risk types simultaneously. Rather than implementing separate detection systems for different risks, a single integrated approach identifies various risks based on the same sensor input, reducing overall system complexity while maintaining comprehensive detection capability.
Solution Approach 2:
The system pre-establishes risk identification algorithms and response protocols before actual risk events occur. By having predefined methods for analyzing sensor signals and modifying autonomous capabilities, the system reduces real-time computational complexity while maintaining high detection capability.
3Reliability
If the autonomous driving capability is modified in response to identified risks, then vehicle safety and hazard avoidance are improved, but control system complexity and response time requirements increase
Solution Approach 1:
The system pre-configures multiple autonomous driving capability modification options and response protocols before risk events occur. When risks are identified, the system can quickly select and implement pre-planned responses rather than computing new solutions in real-time, thereby reducing response time while maintaining comprehensive hazard avoidance capability.
Solution Approach 2:
The system implements dynamic modification of autonomous driving capabilities that adapts the level of intervention based on risk severity. For minor risks, small adjustments are made, while for critical risks, more significant modifications are implemented. This dynamic approach optimizes response time by matching the complexity of the response to the urgency of the situation.
Data Source
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AI summary
Among other things, sensor signals are received using a vehicle comprising an autonomous driving capability. A risk associated with operating the vehicle is identified based on the sensor signals. The autonomous driving capability is modified in response to the risk. The operation of the vehicle is updated based on the modifying of the autonomous capability.