Autonomous Vehicle Emergency Response Trajectory Planning
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Solution Overview
Problem
Autonomous vehicles face challenges in effectively responding to emergency vehicles, particularly when time is critical, as they need to yield right of way and ensure passenger safety during emergencies.
Innovation Solution
The system employs sensor systems to detect emergency vehicles, determines whether to handle the response autonomously or invoke remote assistance, generates an encoded representation of the response, and plans a trajectory based on this representation, using machine learning models and path planning algorithms to navigate safely around the emergency vehicles.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If autonomous vehicles use sensor systems and machine learning models to detect and respond to emergency vehicles, then road safety and passenger protection are improved, but system complexity and response time requirements increase
Solution Approach 1:
The system segments the emergency vehicle response problem into distinct modules: sensor detection subsystem, machine learning classification subsystem, trajectory planning subsystem, and execution subsystem. Each module handles a specific aspect of the response process, making the overall complex system manageable and reliable.
Solution Approach 2:
The system performs preliminary actions by pre-training machine learning models with emergency vehicle detection data and pre-computing multiple trajectory options before an actual emergency situation arises. This preparation enables faster and more reliable response when an emergency vehicle is detected.
2Speed
If autonomous vehicles autonomously handle emergency vehicle detection and response, then response speed is improved, but reliability may decrease due to potential autonomous system failures
Solution Approach 1:
The system introduces a communication interface as an intermediary between the autonomous vehicle and remote assistance operators. This allows the autonomous system to quickly detect and respond to emergency vehicles while maintaining the option to escalate to human operators for verification or additional instructions, thereby balancing speed and reliability.
3Measurement precision
If autonomous vehicles generate encoded representations and plan trajectories in real-time, then navigation accuracy around emergency vehicles is improved, but computational load and processing time increase
Solution Approach 1:
The system applies partial action by generating encoded representations of only the critical trajectory parameters needed for emergency vehicle response rather than full navigation paths. This reduces computational load while maintaining sufficient navigation accuracy for the specific emergency response context.
4Reliability
If autonomous vehicles integrate remote assistance mechanisms, then response reliability is improved, but system complexity and communication requirements increase
Solution Approach 1:
The system implements self-service by enabling the autonomous vehicle to independently handle routine emergency vehicle detection and response using its sensor systems and machine learning models. Remote assistance is invoked only when the autonomous system requires verification or additional guidance, reducing the overall complexity burden while maintaining reliability.
Data Source
AI summary
Disclosed are systems and methods for responding to detected emergency vehicles. In some aspects, a method includes generating an emergency vehicle (EMV) detection signal based on sensor signals of an autonomous vehicle (AV); determining, based on the EMV detection signal, whether remote assistance (RA) is invoked for the EMV signal or whether the AV is to handle the EMV detection signal autonomously; and responsive to determining that the AV is to handle the EMV detection signal autonomously, determining a trajectory of the AV in response to the EMV detection signal.