Traffic Lane Localization using Clothoid Road Model

In the past few years, exponential growth in autonomous driving vehicles has been observed due to its features such as efficient vehicles in a medical emergency, traffic avoidance, traveling time, fuel consumption, etc. Lane level localization, being an indigenous part of self-driving machines, uncovers a wide area of research and scope for cutting-edge technologies. Although it comes up with many advantages, still it faces a few challenges such as improper road structures, low visibility at night, shadows on road, and traffic vehicle intervention. To overcome these challenges, many sensors and technologies have emerged in the past few years such as Light Detection and Ranging (LiDAR), Global Positioning System (GPS), Inertial Measurement Unit (IMU), cameras, etc. In the present scenario, the demand has increased rapidly for an accurate technology with a low installation cost. Vision-based technologies have brought revolutionary changes by accurate lane detection as well as rapid accident-avoidance features with low-cost installation. In this work, Radio Frequency (RF) sensors along with a camera have been used for path detection under traffic conditions. A clothoid road model has been adapted to define the motion path for a vehicle over the cubic spline and trigonometric spline model due to its ease in tracking and linear steering motion. Clothoid parameters along with traffic information have been evaluated using vision-based data. Relative traffic vehicle position and velocity have been computed using RF sensor data. Low-cost installation of sensors and precise lane detection under heavy traffic conditions made this approach suitable over other methods.