Expressway Lane Change Decision Model for Networked Safety
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
The increasing number of vehicles on expressways leads to rising traffic pressure and safety concerns due to unsafe lane changing behaviors, which can result in traffic congestion and accidents, especially in networked environments where vehicles interact with intelligent facilities.
Innovation Solution
A method for determining the risk type and direction of lane changes for vehicles on expressways by analyzing the position, speed, and acceleration of target vehicles and surrounding vehicles, using equations to calculate lane change risk factors and direction factors, thereby optimizing lane changes and improving safety.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If drivers perform lane changing based on individual characteristics and driving experience, then driving efficiency may be improved, but traffic safety deteriorates due to unsafe lane changing behavior
Solution Approach 1:
The system implements feedback by continuously monitoring lane change risk factors and providing real-time guidance to drivers. The roadside intelligent facilities collect data on vehicle positions, speeds, and lane conditions, then feed this information back to the target vehicle's terminal device, enabling drivers to make informed lane change decisions that balance efficiency and safety
Solution Approach 2:
The patent introduces an intermediary system consisting of roadside intelligent facilities and terminal devices that mediates between individual driver decisions and overall traffic safety. This intermediary calculates lane change risk factors based on comprehensive traffic data and provides objective guidance, reducing the reliance on subjective driving experience while maintaining driving efficiency
2Ease of operation
If vehicles engage in unsafe lane changing behavior, then individual driving flexibility is improved, but traffic congestion and accidents increase
Solution Approach 1:
The system applies preliminary anti-action by calculating lane change risk factors before the actual lane change occurs. The roadside intelligent facilities assess potential hazards in advance and provide warnings or guidance to prevent unsafe lane changes, thereby reducing traffic friction and accidents before they can happen
Solution Approach 2:
Real-time feedback on lane change risk factors enables drivers to adjust their lane changing behavior dynamically. The system continuously monitors traffic conditions and provides immediate information about safety risks, allowing drivers to maintain flexibility while avoiding harmful lane changing actions
3Reliability
If networked vehicles interact with roadside intelligent facilities to calculate lane change risk factors, then lane changing safety is improved, but system complexity increases
Solution Approach 1:
The terminal device on the target vehicle performs self-service by automatically receiving and processing lane change risk factor data from roadside intelligent facilities. The system autonomously calculates risk assessments and provides guidance without requiring complex manual interventions, thereby improving safety while keeping the user interface simple
Solution Approach 2:
The system segments the lane change safety function into separate components: roadside intelligent facilities for data collection and risk calculation, communication systems for data transmission, and terminal devices for presenting guidance to drivers. This segmentation improves safety through specialized functions while managing overall system complexity through modular design
Data Source
AI summary
Embodiments of the present disclosure provide a method for lane changing decision for vehicles on expressways considering safety risks in a networked environment, which is applicable to a multi-lane expressway. The method includes collecting a demand for lane change of a target vehicle; collecting a roadway and a lane where the target vehicle is located; collecting intervals between the target vehicle and a front vehicle and a rear vehicle on a lane that is switching into; determining a lane change direction of the vehicle; and executing a lane change operation by the target vehicle. A lane changing decision model provided in the embodiments of the present disclosure helps to improve the efficiency of lane change and the safety of lane change in expressways and provides a support for changing lanes in a multi-lane expressway.