Autonomous Vehicle Control Planning With RSS Risk Diagnostics
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Solution Overview
Problem
Current automatic driving systems face challenges in ensuring safety by accurately calculating potential accident liability values and determining appropriate control plans, particularly in dynamic traffic environments where inter-vehicle distances and road structures vary.
Innovation Solution
A vehicle control system that integrates surrounding monitoring sensors, map data, and diagnostic devices to assess risk and adjust control plans dynamically, using a Responsibility Sensitive Safety model to evaluate potential accident liability and determine safe distances based on real-time traffic conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the planner calculates potential accident liability values for multiple control plans using map data and road structure information, then the safety evaluation becomes more comprehensive, but the calculation time and processing complexity increase
Solution Approach 1:
The system pre-calculates and stores safety distance information for different road structures (intersections, curves, straight sections) before actual driving. When generating control plans, the planner directly retrieves these pre-computed safety distances rather than calculating them in real-time, significantly reducing processing time while maintaining comprehensive safety evaluation
Solution Approach 2:
The road structure is divided into discrete segments (intersections, curves, straight sections) with predetermined safety distance characteristics. The planner evaluates each segment independently and aggregates the results, enabling efficient parallel processing and reducing overall calculation complexity while maintaining comprehensive safety coverage
2Measurement precision
If the system determines safe distances based on detailed road structure analysis and inter-vehicle distance monitoring, then the potential accident liability calculation becomes more accurate, but the device complexity increases
Solution Approach 1:
The surrounding monitoring device performs multiple functions: detecting inter-vehicle distances, identifying road structures, and providing input to both the planner and diagnostic device. This multi-functionality reduces the need for separate dedicated sensors and processing units, thereby reducing overall system complexity while maintaining high measurement precision
Solution Approach 2:
The diagnostic device acts as an intermediary that receives data from the surrounding monitoring device, validates it, and provides processed information to the planner. This intermediary layer simplifies the data flow and reduces the complexity burden on the main planning system by handling validation and preprocessing tasks separately
Data Source
AI summary
An automatic drive device includes a fusion unit recognizing travel environment of a vehicle based on an output signal of a surrounding monitoring sensor and a control planning unit generating a control plan based on a recognition result of the fusion unit. A diagnostic device includes an abnormality detection unit that detects an abnormality of a field recognition system from the surrounding monitoring sensor to the fusion unit by monitoring the output signal of the surrounding monitoring sensor and the recognition result of the fusion unit over time. The diagnostic device requests the control planning unit to perform MRM based on a detection of the abnormality of the recognition system by the abnormality detection unit.