Fuzzy Logic Based Advanced Driving Assistant with Explainable Intelligence for Autonomous Navigation in Multilane Environment

Fuzzy logic techniques are largely in Autonomous vehicles to address complications and challenges around safety and efficiency of these self-driving vehicles and the driver. The Society of Automotive Engineers has classified self-driving vehicles into stages, which range from level 0 to 5. Particularly, a level 2 vehicle is one which includes partial automation where the vehicle can perform steering and/or acceleration. At this level, a human monitor all the tasks and an individual can overtake the automated system at any time. Currently most of the driver assistance technology give warning signals such as vehicles approaching in the blind spot, turning the lights on in a foggy weather, automatic parking, lane departure warning, warning signal when getting close to the vehicles etc. The current solutions do not navigate the vehicles in a complex multi-lane environment with a variety of vehicles on the road such as trucks and cars and traffic merging from the ramp, imposed speed limits, traffic lights, uphill etc. Therefore, there is a need to develop a complete navigation system that mimics the logic of the driver and allows for hands off driving.

Invention Summary:

Researchers at the University of Toledo have developed an advanced driving assistance system using fuzzy logic comprising of a fuzzy inference system for level 2 autonomous vehicles that can control both speed control and lane changing simultaneously. Our fuzzy interference system takes distance and relative distance from the car in front into consideration and gives the output of speed control. Further, the system provides the ability to control several control parameters such as number of vehicles entering the lanes, ratio of trucks to cars, speed limit imposed, and distance required between the vehicles, acceleration and deceleration, politeness factor etc. The automated driving system has been tested using simulation under different scenarios including uphill, traffic signals, ring road, lane closing, on ramp, etc.


Autonomous vehicles.


  • Significant improvement in decision making especially in an multi lane environment.
  • Mimics the logic of the driver with explainable intelligence of the decision-making process.
  • The software of Autonomous Navigation System is available.

IP Status: Patent Pending



Contact Information

TTO Home Page: http://utoledo.technologypublisher.com

Name: Lokesh Mohan

Title: Licensing Associate

Department: Technology Transfer

Email: lokeshwar.mohan@utoledo.edu

Phone: 419-530-6231