Road Disturbance Estimation and Cloud-Aided Comfort-Based Route Planning.

Area of Science:

• Automotive Engineering
• Robotics and Control Systems
• Transportation Systems

Background:

• Traditional route planners prioritize travel time, often neglecting ride comfort.
• Vehicle sensors can be leveraged for real-time road condition assessment.
• Objective metrics for ride comfort are needed for advanced route planning.

Purpose of the Study:

• To develop a comfort-based route planner integrating travel time and ride comfort.
• To enable simultaneous road profile estimation and anomaly detection using vehicle sensors.
• To formulate and solve the comfort-based route planning problem.

Main Methods:

• A jump-diffusion process-based state estimator and multi-input observer for road profile estimation.
• Development of three objective comfort metrics considering travel time, road roughness, anomalies, and intersections.
• Formulation of the comfort-based route planning problem solved using an extended Dijkstra's algorithm.
• Cloud-based implementation for efficient data access and computation.

Main Results:

• The road profile estimation framework demonstrated promising performance in experimental tests.
• The proposed comfort metrics effectively quantify ride quality factors.
• The extended Dijkstra's algorithm successfully solved the comfort-based route planning problem.
• A real-world case study validated the framework's efficacy.

Conclusions:

• The developed framework enables comfort-based route planning by integrating road condition estimation and objective comfort metrics.
• The approach offers a significant advancement over traditional route planning methods by prioritizing passenger comfort.
• The cloud-based implementation ensures scalability and accessibility for practical applications.