Traffic-Light-Aware Vehicle Routing to Reduce Chain Waiting
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Solution Overview
Problem
Existing vehicle route determination methods do not consider traffic light timing and phase information, leading to inefficient travel times due to unnecessary stops at traffic lights.
Innovation Solution
A method and system that utilize traffic-light data, vehicle-telemetry data, and map data to identify instances of chain waiting and determine the fastest route by incorporating traffic-light timing and phase information, minimizing stops and optimizing travel time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If traditional route determination methods are used without traffic light information, then the routing system is simpler, but the overall travel time increases due to unnecessary stops at traffic lights
Solution Approach 1:
The system performs preliminary actions by obtaining traffic light timing and phase information in advance before route determination. The server receives and stores traffic light data including timing information and phase information, allowing the routing algorithm to predict future traffic light states and plan routes that minimize stops, rather than reacting to current traffic light states only
Solution Approach 2:
A server acts as an intermediary between traffic light infrastructure and the routing system. The server collects traffic light data from multiple sources, processes it, and provides it to the routing determination system, enabling complex traffic light-aware routing without directly complicating individual vehicle or router systems
2Productivity
If traffic light timing information is incorporated into route determination, then travel time through traffic lights is reduced, but the data processing and system complexity increase
Solution Approach 1:
The system segments the routing problem by identifying chain waiting instances separately - detecting when a vehicle will stop at one traffic light and subsequently stop at another traffic light in sequence. This segmentation allows the system to specifically target and optimize for multi-stop scenarios rather than treating all stops equally, improving efficiency without proportionally increasing complexity
Solution Approach 2:
The system uses vehicle telemetry data as feedback to validate and refine traffic light timing predictions. By comparing predicted traffic light states with actual vehicle stopping behavior from telemetry data, the system can adjust and improve its routing recommendations, creating a self-improving system that manages complexity through learning
Data Source
AI summary
A method for determining the route of a vehicle includes receiving, by a vehicle controller of a host vehicle, traffic-light data, map data, and vehicle-telemetry data. Further, the method includes identifying instances of chain waiting along the plurality of routes in the predetermined area using the traffic-light data and the vehicle-telemetry data and determining the fastest route of the plurality of routes to travel from a starting point to an end point within the predetermined area using the map data, the instances of chain waiting along the plurality of routes, the vehicle-telemetry data, and the traffic-light data.