Mixed Traffic Control Using CAV Commands and Smart Traffic Signs
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Solution Overview
Problem
Existing traffic control systems struggle to optimally manage the motion of connected autonomous vehicles (CAVs) and manual connected vehicles (MCVs) jointly, as CAVs require continuous control for optimization, while MCVs are controlled indirectly through traffic signs, leading to limited control over the transportation network.
Innovation Solution
A traffic control system that collects digital representations of states from CAVs, MCVs, and traffic signs, and solves a multi-variable mixed-integer problem to optimize control commands for both CAVs and traffic signs, considering individual and common objectives of the vehicles and adhering to general traffic rules.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If CAVs are controlled continuously at any point in time and space to optimize transportation network, then travel time and traffic flow are improved, but system complexity and control difficulty increase
Solution Approach 1:
The control system segments the transportation network into discrete zones with embedded controllers that independently manage local traffic flow. Each zone controller processes CAV commands and coordinates with adjacent zones, dividing the complex global optimization problem into manageable local decisions while maintaining overall network efficiency.
Solution Approach 2:
The system adds a spatial dimension to control by deploying distributed zone controllers throughout the network rather than centralized control. This dimensional distribution allows parallel processing of traffic optimization across multiple locations simultaneously, reducing computational complexity while maintaining comprehensive optimization capability.
2Ease of operation
If MCVs are controlled indirectly through traffic signs, then control is simplified and easier to implement, but control effectiveness and optimization capability are limited
Solution Approach 1:
The system introduces smart traffic signs as intermediary devices that receive direct commands from the control system and translate them into visible instructions for MCV drivers. These signs act as mediators between the digital control architecture and human drivers, enabling indirect control of MCVs while maintaining optimization effectiveness through real-time instruction delivery.
Solution Approach 2:
The system replaces traditional mechanical traffic control methods with electronic communication infrastructure. Digital commands are transmitted electronically to smart traffic signs and CAVs, substituting physical mechanical control systems with electronic information-based control, thereby improving both ease of operation and optimization effectiveness.
3Productivity
If CAVs control motion of MCVs by occupying lanes in specific ways, then traffic flow is improved, but safety risks and negative effects on CAV operations increase
Solution Approach 1:
The system implements continuous feedback loops where zone controllers monitor the positions, speeds, and intentions of both CAVs and MCVs in real-time. Based on this feedback, the controllers dynamically adjust lane occupancy strategies and traffic instructions to maintain safe distances and prevent dangerous situations, ensuring that traffic flow optimization does not compromise safety.
Solution Approach 2:
The control system performs preliminary actions by predicting future traffic states and proactively adjusting lane assignments and traffic instructions before dangerous situations arise. By anticipating potential conflicts between CAVs and MCVs, the system preemptively optimizes lane occupancy patterns to prevent safety issues while maintaining traffic flow efficiency.
Data Source
AI summary
The present disclosure provides a system and a method for jointly controlling one or multiple connected autonomous vehicles (CAVs) and one or multiple manual connected vehicles (MCVs) moving to form traffic on the same or intersecting roads. The method includes collecting states of each of the CAVs, each of the MCVs, and each of traffic signs regulating the traffic, and solving a multi-variable mixed-integer problem (MIP) optimizing a cost function for values of control commands changing states of each CAV and values of control commands changing states of each of the traffic signs. The cost function is optimized subject to a motion model of each of the CAVs, subject to constraints modeling general traffic rules, subject to timing constraints, and subject to a motion model of each MCV. The method further includes transmitting the optimized values of the control commands to the corresponding CAVs and corresponding traffic signs.