Lane-Change Planning Using Target-Lane String Stability
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Solution Overview
Problem
Lane-change actions by vehicles can lead to traffic instabilities, resulting in stop-and-go waves and reduced road capacity, necessitating systems and methods that consider traffic stability during lane changes.
Innovation Solution
A system and method for predicting traffic stability in a target lane by learning a car-following model, simulating vehicle movements, and determining string stability, with the ego vehicle changing lanes if stable and activating mitigation strategies if unstable.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If vehicles perform lane-change actions to improve route following or trip experience, then individual vehicle efficiency is improved, but traffic stability deteriorates leading to stop-and-go waves and congestion
Solution Approach 1:
The system performs preliminary simulation of vehicle movements in the target lane before executing a lane-change action. The car-following model predicts how other vehicles will respond to the impending lane change, allowing the ego vehicle to assess potential traffic instability consequences before acting, thus resolving the contradiction by preparing advance information about traffic stability impacts
Solution Approach 2:
The system incorporates feedback from the car-following model simulation results into the lane-change decision-making process. By continuously monitoring and simulating the potential impact on target lane vehicles, the system adjusts lane-change actions to maintain traffic stability while still achieving individual vehicle efficiency goals
2Ease of operation
If lane-change actions are executed without considering target lane stability, then lane-change speed and decision-making simplicity are improved, but traffic congestion and stop-and-go waves increase
Solution Approach 1:
The system enables the ego vehicle to autonomously assess traffic stability impacts using an integrated car-following model. The vehicle self-evaluates the potential consequences of its lane-change action on target lane traffic before executing the maneuver, maintaining operational simplicity while protecting road capacity through automated stability assessment
3Reliability
If connected vehicles plan lane-change actions considering traffic stability, then overall traffic performance is improved, but computational complexity and processing requirements increase
Solution Approach 1:
The system uses parameter changes in the car-following model to assess traffic stability. By varying key parameters such as time headway, speed differential, and spatial positioning within the simulation, the system evaluates stability conditions without requiring complex computational analyses, thus improving reliability while controlling computational complexity
Data Source
AI summary
A method for planning lane-change actions considering string stability in a target lane is provided. The method includes learning a car following model based on training data related to a region, obtaining information about vehicles in a target lane in the region, simulating movements of the vehicles in the target lane responsive to an ego vehicle moving into the target lane based on the learned car following model and the information about the vehicles, determining whether the vehicles in the target lane will be string stable responsive to the ego vehicle moving into the target lane based on the simulated movements of the vehicles in the target lane, and instructing the ego vehicle to change lanes from a current lane to the target lane in response to determining that the vehicles in the target lane will be string stable responsive to the ego vehicle moving into the target lane.