Lookup Table Consensus for Connected Vehicle Coordination
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Solution Overview
Problem
Existing consensus algorithms for Connected and Automated Vehicles (CAVs) fail to adequately address real-world constraints such as safety, efficiency, and comfort, particularly in tuning control gains to satisfy distance headway requirements and respond to varying initial vehicle states.
Innovation Solution
A lookup table-based consensus mechanism that populates control gain values based on driving constraints, including safety, efficiency, and comfort, to coordinate the behavior of vehicles in Cooperative Adaptive Cruise Control (CACC) applications, allowing for real-time adjustment of control gains to match initial vehicle states.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a consensus algorithm is implemented to coordinate vehicle behavior, then vehicle coordination is achieved, but convergence time is excessive
Solution Approach 1:
The patent pre-calculates and stores optimal control gain values in lookup tables before runtime. When the consensus algorithm needs to coordinate vehicles, it directly retrieves pre-computed gains from the lookup table based on current state, avoiding iterative calculation during execution. This preliminary preparation significantly reduces convergence time while maintaining coordination reliability.
Solution Approach 2:
The patent implements dynamic adjustment of control gain values based on real-time vehicle states (distance headway, speed differences). The system selects different control gains from the lookup table according to the current operating conditions, allowing the consensus algorithm to adapt quickly to changing states and achieve faster convergence across various scenarios.
2Loss of time
If control gain values are tuned for fast convergence, then convergence time is reduced, but safety constraints are violated
Solution Approach 1:
The patent organizes control gain values in lookup tables indexed by safety-relevant parameters such as distance headway and speed differences. Each entry in the lookup table contains control gains that are pre-validated to satisfy safety constraints for that specific parameter range. By changing parameters based on current state and selecting corresponding pre-validated gains, the system achieves fast convergence without violating safety requirements.
Solution Approach 2:
The patent applies different control gain values for different operating conditions (local states). Instead of using a single fixed control gain, the system selects locally optimized gains from the lookup table that are appropriate for the current distance headway and speed difference. This local customization ensures safety constraints are met for each specific situation while enabling fast convergence.
3Productivity
If control gains are increased to reduce convergence time, then efficiency is improved, but ride comfort deteriorates due to excessive jerk
Solution Approach 1:
The patent creates lookup tables that are multidimensional, indexed not only by distance headway and speed but also by other state parameters. The pre-computed control gains in the lookup table are optimized to balance convergence speed and jerk limitation. By selecting appropriate parameters from the lookup table based on current state, the system achieves efficient convergence while keeping jerk within acceptable limits for ride comfort.
Solution Approach 2:
The patent pre-calculates control gain values that simultaneously optimize multiple objectives: convergence speed and jerk limitation. These multi-objective optimized gains are stored in the lookup table and retrieved based on current vehicle states. This approach resolves the trade-off between efficiency and comfort by having pre-computed gains that achieve both goals for different operating conditions.
Data Source
AI summary
The disclosure includes embodiments for providing a lookup table to improve performance of a consensus mechanism used in Connected and Automated Vehicle (CAV) technologies. In some embodiments, a method for a connected vehicle includes building a lookup table that is populated with control gain values for a consensus mechanism. The control gain values are created based on a satisfaction of one or more driving constraints by the consensus mechanism. The method includes receiving data describing an initial vehicle state related to a flow of vehicles. The method includes searching the lookup table for a set of control gain values based on the initial vehicle state. The method includes implementing the consensus mechanism on the flow of vehicles based on the set of control gain values so that behavior of the flow of vehicles is coordinated.