Lane Recommendation Module Using Sensor Fusion for Traffic Flow
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Solution Overview
Problem
Current navigation systems do not address traffic flow at a vehicle level, failing to recommend lanes that are flowing more efficiently, which can lead to increased travel times, fuel consumption, and environmental pollution.
Innovation Solution
Implementing a sensor fusion module within vehicles to collect and process data from various sensors, such as radars and LIDAR, to identify a preferred travel lane based on traffic metrics like speed, density, and bunching, and recommend it to the driver or autonomous system.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If GPS-based navigation systems recommend routes based on traffic conditions, then travel route planning is improved, but lane-level traffic flow efficiency is not addressed
Solution Approach 1:
The patent segments the navigation system into two levels: route-level navigation (existing GPS functionality) and lane-level optimization (new sensor fusion module). The sensor fusion module independently processes sensor data to evaluate traffic flow in different lanes, allowing lane recommendations without disrupting the overall route planning function.
Solution Approach 2:
The sensor fusion module serves multiple functions: it processes data from various sensors (radar, LIDAR, cameras), evaluates traffic metrics across multiple lanes, generates lane recommendations, and can adapt to different driving conditions. This multi-functional module addresses lane-level efficiency while working within the existing GPS navigation framework.
2Productivity
If sensor fusion modules process data from multiple sensors to identify preferred lanes, then traffic flow efficiency is improved, but system complexity increases
Solution Approach 1:
The sensor fusion module continuously processes sensor data in advance to build a real-time model of traffic conditions across all lanes. By pre-evaluating traffic metrics (speed, density, acceleration) and maintaining updated lane flow profiles, the system is ready to provide immediate lane recommendations when the vehicle approaches decision points, reducing computational burden during critical moments.
Solution Approach 2:
The sensor fusion module acts as an intermediary layer between raw sensor data and the navigation system. It aggregates and processes data from multiple sensors, transforms it into meaningful traffic metrics, and presents processed information to the lane recommendation logic, simplifying the overall system architecture by creating a dedicated data processing intermediate layer.
3Loss of time
If the system continuously monitors traffic conditions to recommend optimal lanes, then travel time is reduced, but energy consumption increases
Solution Approach 1:
The sensor fusion module operates periodically rather than continuously, updating lane recommendations at strategically determined moments (e.g., when approaching lane change opportunities or when traffic conditions significantly change). This periodic operation reduces energy consumption from continuous processing while still capturing sufficient traffic flow information to identify time-saving lane opportunities.
Solution Approach 2:
The system uses existing sensor data collected for other purposes (collision avoidance, adaptive cruise control) and repurposes it for lane recommendation. By leveraging data already being collected by the vehicle's sensor suite, the system reduces additional energy expenditure while maintaining its ability to identify optimal lanes for reducing travel time.
Data Source
AI summary
Various embodiments include an automated travel lane recommendation implemented for a vehicle in response to traffic conditions in the vehicle's vicinity, including characteristics of traffic flow of nearby lanes of travel. In some examples, sensors implemented as part of a vehicle collect data about available lanes and other vehicles and obstructions in the vicinity of the vehicle or along the vehicle's route of travel. According to some examples, sensor fusion logic processes sensor data to calculate metrics associated with traffic conditions relevant to the vehicle. In some embodiments, a preferred travel lane for the vehicle is calculated using a combination of a cost function of traffic metrics with other available information. The preferred travel lane is presented to an operator or control system of the vehicle in some examples. Information, such as preferred lane information, cost function information, and traffic condition metrics is shared with other vehicles and devices, or stored to a database in some examples.