The most commonly used navigation systems often use vehicle density and distance to select the shortest route. But road conditions also play a vital role in determining the same. There are instances where we reach a place and find out that the road is either small or in a very poor condition and hence the information obtained was false. This project mainly focuses on the collection of data or information on a specified travel route based on which a person can choose or identify the best and most comfortable route among the several possible routes. The basic idea is to gather data like the road’s width, the number of humps, potholes, etc. For the same, we use a suitable highresolution fused camera and LiDAR sensor interfaced to a Jetson nano board and mount the same on a vehicle preferably a car. The data hence collected can either be stored using an onboard storage device like a hard disk or can directly be fed to the server or cloud using a WiFi module as required. The sensors will be mounted on the vehicle so that we can scan the entire span of the road in a single run and collect video and depth perspective data simultaneously for further analysis and computations. Since the sensors are scanning the road, they are to be kept facing the road i.e., downwards, this somewhat resembles the way the headlight of the vehicle lights the road. The camera here gives us an overall view of the number of obstacles that are present on the road. LiDAR gives the depth data which gives a more accurate identification of the pothole. The collected data are computed or processed using the YOLO algorithm. The programming part is done using Python. The collected data is suitably filtered and compiled into a database that can be used for future reference.