Driving Behavior Analysis of Intelligent Vehicle System for Lane Detection Using Vision-Sensor

Lane detection on the road is the most essential phase of existing advanced driver assistance systems (ADAS). In the current road scheme, the lanes are parallel tracks which are marked based on the different speed limit for the vehicles. Lane markings are defined as driving regions for the vehicles in order to avoid a collision. In an Intelligent Vehicle System (IVS), these markings are being identified by various massive computing techniques and sensor-based mechanisms like radar, LiDAR, and GPS and have high operational costs. However, vision-based detection methods are gaining popularity due to its low cost and better understanding capability of the scene. The proposed study follows computer vision and image processing techniques to examine and learn significant lane features. The approach demonstrates the practical and effective usage of the lane detection model (LDM). The LDM has been designed with a vehicle prototype using Raspberry Pi, which reveals the intelligent behavior against the detected lane lines. The model is less expensive and power-efficient, which confirms the operational capability of the proposed system under a heterogeneous environment.

Automated summary

This study uses computer vision and image processing techniques to examine key lane features, designing a low-cost and power-efficient lane detection model using Raspberry Pi, showcasing its operational capability in a heterogeneous environment.