LiDAR Fusion-based Optimized Path Selection for Autonomous Vehicles using the Verification Model

Autonomous vehicles use LiDAR sensors to navigate 3D maps of their surroundings, enabling improved decision-making and road control systems. It utilizes laser light to measure the speed, distance, shape, direction, and perturbation of any 3D object in a point cloud. Data on point clouds is in high resolution and used to identify different objects of regular or irregular shapes. It is also used in safe path planning, path optimization, and object avoidance routes. Although point cloud data is robust and beneficial for safe navigation, errors and inconsistencies in the interpretation lead to erroneous judgments. Processing wrong information potentially resulting in hazardous circumstances with severe consequences of casualties and road accidents. In contrast, each path planning and path optimization policy should be thoroughly tested and validated before being applied to real-world scenarios. To provide a safer and collision-free path and to verify optimized path policy, we have proposed a technique called ”LiDAR Fusion-based Optimized Path Selection (LOPS) for Autonomous Vehicles using the Verification Model.” The proposed method uses an object detection strategy and instructs autonomous vehicles to use optimized paths for safe travel and collision-free path selection. The proposed scheme is formally modeled and verified for defined states in UPPAAL, and each state with defined properties has been checked. LOPS has been evaluated with different parameters, including the jerking rate, which ranges from 1.0% to 0.06% in various densities of autonomous vehicles. We have also calculated the effect of optimization of the path on the distance covered in unit time, and it is found that 50 vehicles require 20% more time to cover 50 km than 10, 5, or 2 vehicles. The lane usage is calculated in unit time and shows that using optimization in the lane scenario, lane 2 is 5-19 times more used in 5 minutes. LOPS is evaluated with two datasets, CARLA and NuScenes for different parameters, including path length, path smoothness, object avoidance success rate, path tracking accuracy, and decision delay comparisons.