Cooperative Driving Control for Mixed-Autonomy Vehicle Traffic
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Solution Overview
Problem
Autonomous vehicles struggle to effectively coordinate with varying levels of autonomy and dynamic factors among surrounding vehicles, impacting traffic efficiency and safety.
Innovation Solution
Implementing a Vehicle Autonomous Driving System (VADS) that uses sensor information and V2V communications to determine Autonomous Capability Metrics (ACMs) for surrounding vehicles, adjusting driving parameters and forming cooperative driving engagements based on these metrics to enhance safety and efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If autonomous vehicles form cooperative driving engagements with varying levels of autonomy, then traffic efficiency and safety are improved, but coordination complexity and communication requirements increase
Solution Approach 1:
The system changes the parameter of autonomy level classification, categorizing vehicles into different levels (fully manual, partial automation, high automation, full automation) to simplify coordination complexity while enabling efficient cooperative driving engagements among vehicles with varying capabilities
Solution Approach 2:
The cooperative engagement system is designed to work universally with vehicles at all autonomy levels, allowing the same communication framework and coordination protocols to handle diverse vehicle types, thereby improving traffic efficiency without requiring separate systems for each vehicle type
2Reliability
If autonomous vehicles communicate and coordinate with surrounding vehicles, then safety and operational advantage are improved, but communication overhead and processing requirements increase
Solution Approach 1:
The system extracts and processes only the essential autonomy level information and engagement status from vehicle communications, filtering out unnecessary data to reduce communication overhead and processing requirements while maintaining safety and operational advantage
Solution Approach 2:
The system implements feedback mechanisms where vehicles share their autonomy level and engagement status, allowing the autonomous vehicle to adjust its behavior based on real-time information from surrounding vehicles, improving safety without requiring continuous high-bandwidth communication
Data Source
Figure 1A~1B
Figure 1c
Figure 2
AI summary
Methods, devices and systems enable controlling an autonomous vehicle by identifying vehicles that are within a threshold distance of the autonomous vehicle, determining an autonomous capability metric of each of the identified vehicles, and adjusting a driving parameter of the autonomous vehicle based on the determined autonomous capability metric of each of the identified vehicles. Embodiments may further include determining, based on the determined ACMs, whether one or more identified vehicles would provide an operational advantage to the autonomous vehicle in a cooperative driving engagement, and initiating a cooperative driving engagement with the one or more identified vehicles in response to determining that the one or more identified vehicles would provide an operational advantage to the autonomous vehicle in a cooperative driving engagement.