Ego-Vehicle Warning for Collisions Between Surrounding Vehicles
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Solution Overview
Problem
Existing vehicle collision warning systems focus primarily on the ego-vehicle's collision risk with surrounding vehicles or obstacles, failing to account for the adverse impact of collisions between other surrounding vehicles, leading to reduced safety warning capabilities and increased risk of involvement in accidents.
Innovation Solution
An ego-vehicle warning method that determines a nearby area based on moving information, classifies surrounding vehicles into nearby and remote categories, calculates the shortest distance between remote and nearby vehicles, and sends alarm information to the driver when a potential collision risk is identified, enabling proactive avoidance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the existing vehicle warning method focuses only on direct collision risk between the ego-vehicle and surrounding vehicles, then the calculation complexity is reduced and the response time is shortened, but the safety warning capability is reduced and the ego-vehicle may be involved in accidents caused by collisions between surrounding vehicles
Solution Approach 1:
The system segments surrounding vehicles into different categories: nearby vehicles (potential collision targets) and remote vehicles (potential collision sources). This segmentation allows the system to focus computational resources on relevant vehicles while maintaining comprehensive safety monitoring, resolving the contradiction between comprehensive safety warning capability and calculation complexity.
Solution Approach 2:
The system performs preliminary classification of surrounding vehicles into nearby and remote categories before conducting collision risk calculations. By pre-identifying which vehicles require detailed analysis and which can be monitored at a distance, the system reduces overall calculation complexity while maintaining comprehensive safety warning capability.
2Reliability
If the system monitors all surrounding vehicles for potential collision risks, then the safety warning capability is improved, but the computational load increases and response time may be delayed
Solution Approach 1:
The system applies different monitoring intensities to different vehicles based on their spatial relationship with the ego-vehicle. Nearby vehicles receive intensive real-time collision risk analysis, while remote vehicles receive periodic classification and monitoring. This local differentiation of monitoring quality maintains high safety warning capability for critical situations while reducing overall computational load and response time.
Data Source
AI summary
An ego-vehicle warning method includes determining a nearby area of an ego-vehicle based on moving information of the ego-vehicle, performing target classification on vehicles surrounding the ego-vehicle to obtain nearby vehicles in a plurality of directions and remote vehicle sets in the plurality of directions, for any remote vehicle in a remote vehicle set, calculating a shortest distance between the remote vehicle and a nearby vehicle in each direction that matches a direction of the remote vehicle in the plurality of directions in a future time period, and recording a time T when the shortest distance is less than a threshold D, and sending alarm information to a driver of the ego-vehicle based on the recorded time T.