Autonomous Vehicle Scenario Simulation for Safe Action Planning
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current autonomous driving technologies lack the necessary safety measures to ensure functional safety, hindering their adoption and use in consumer applications.
Innovation Solution
An autonomous vehicle management system that utilizes AI and machine learning techniques to control autonomous vehicle operations by generating and updating internal maps based on sensor data, simulating scenarios, and dynamically controlling sensor behavior to ensure safe navigation and decision-making.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If autonomous driving technologies use AI-based technologies to perform operations such as identifying objects and making automatic decisions, then the functionality and automation level are improved, but functional safety is insufficient
Solution Approach 1:
The system performs preliminary actions by generating multiple candidate plans of action before executing any autonomous operation. Each plan is evaluated through simulation of potential scenarios, and safety checks are performed in advance. This allows the system to identify and eliminate unsafe options before actual execution, thereby improving functional safety while maintaining high automation levels.
Solution Approach 2:
The system implements beforehand cushioning by creating a safety buffer through scenario simulation and evaluation. Multiple potential outcomes are predicted and assessed, allowing the system to prepare contingency plans and safety measures in advance. This cushioning approach ensures that even if predictions are uncertain, the system has pre-prepared safe fallback options.
2Reliability
If the autonomous vehicle management system generates and simulates multiple scenarios to ensure safety, then the reliability is improved, but the computational complexity and processing time increase
Solution Approach 1:
The system segments the complex scenario simulation process into distinct modules: generating candidate plans, simulating individual scenarios, evaluating safety outcomes, and selecting optimal actions. This segmentation allows each component to be optimized independently and processed efficiently, reducing overall computational complexity while maintaining comprehensive safety evaluation.
Solution Approach 2:
The system applies partial action by generating a limited number of most-relevant candidate plans rather than exhaustively simulating all possible scenarios. The scenario generation focuses on plausible and high-impact situations, evaluating only the necessary subset of scenarios required to ensure safety, thereby reducing computational burden while maintaining reliability.
3Measurement precision
If the system generates detailed plans of action based on internal maps and safety considerations, then the decision-making quality is improved, but the processing time increases
Solution Approach 1:
The system performs preliminary action by pre-generating and storing internal maps of the environment and pre-evaluating multiple candidate plans during periods when time is less critical. This allows the system to have prepared decision frameworks ready for rapid selection during time-sensitive autonomous operations, improving both decision quality and response time.
Data Source
AI summary
Techniques are described herein for determining one or more actions for an autonomous vehicle to perform, based on simulation of at least one possible scenario. A possible scenario may involve, for example, the autonomous vehicle interacting with an object in the environment. The possible scenario may be simulated by modifying a first internal map containing information about the autonomous vehicle and the environment. As part of the simulation, one or more parameters of the first internal map can be modified in order to, for example, determine the state of the object at a particular point in the future. Based on the modification of the one or more parameters, a second internal map representing a possible scenario is generated from the first internal map. Both the first internal map and the second internal map can be evaluated to decide which action to take.