A Sensor-Fusion Drivable-Region and Lane-Detection System for Autonomous Vehicle Navigation in Challenging Road Scenarios

TLDR

Autonomous vehicle navigation is challenging because diverse real‑world road scenarios must be handled using only perception sensors without positional data. This paper introduces a real‑time optimal‑drivable‑region and lane detection system that fuses LIDAR and vision data. The system uses a multisensory scheme that fuses LIDAR and vision at the feature level, applies an optimal selection strategy to identify the best drivable region, and conditionally executes a lane detection algorithm based on that region’s classification. Experiments demonstrate that the system reliably handles both structured and unstructured roads, confirming its effectiveness and robustness.

Abstract

Autonomous vehicle navigation is challenging since various types of road scenarios in real urban environments have to be considered, particularly when only perception sensors are used, without position information. This paper presents a novel real-time optimal-drivable-region and lane detection system for autonomous driving based on the fusion of light detection and ranging (LIDAR) and vision data. Our system uses a multisensory scheme to cover the most drivable areas in front of a vehicle. We propose a feature-level fusion method for the LIDAR and vision data and an optimal selection strategy for detecting the best drivable region. Then, a conditional lane detection algorithm is selectively executed depending on the automatic classification of the optimal drivable region. Our system successfully handles both structured and unstructured roads. The results of several experiments are provided to demonstrate the reliability, effectiveness, and robustness of the system.