Hundreds of people are injured or killed in road accidents. These accidents are caused by several intrinsic and extrinsic factors, including the attentiveness of the driver towards the road and its associated features. These features include approaching vehicles, pedestrians, and static fixtures, such as road lanes and traffic signs. If a driver is made aware of these features in a timely manner, a huge chunk of these accidents can be avoided. This study proposes a computer vision-based solution for detecting and recognizing traffic types and signs to help drivers pave the door for self-driving cars. A real-world roadside dataset was collected under varying lighting and road conditions, and individual frames were annotated. Two deep learning models, YOLOv7 and Faster RCNN, were trained on this custom-collected dataset to detect the aforementioned road features. The models produced mean Average Precision (mAP) scores of 87.20% and 75.64%, respectively, along with class accuracies of over 98.80%; all of these were state-of-the-art. The proposed model provides an excellent benchmark to build on to help improve traffic situations and enable future technological advances, such as Advance Driver Assistance System (ADAS) and self-driving cars.

Several effective methods of traffic-sign recognition have been around for a lot of time now, starting with recognition using conventional Image Processing techniques which are very generic and sluggish. However, majority of state-of-the-art detectors are based on Convolutional Neural Networks (CNNs) which have been evidenced to be de facto leader in image processing and computer vision research over the past decade. This has been made possible by datasets being easily available, organized and maintained with German Traffic Sign Recognition Benchmark being of relevance. CNNs require colossal amounts of data to work well; unfortunately, no traffic-sign dataset exists in Pakistan to enable any detector based on CNNs. This paper presents an approach revolving around transfer learning whereby, a model is pre-trained using German Traffic-sign Dataset and is then fine-tuned over Pakistani Dataset: which is collected across Pakistan and amounts to 359 images. Preprocessing and regularization are used to improve overall performance of the model. The fine-tuned model reached training accuracies of around 41% with minimal overfitting. This presents an encouraging outcome as even with a dataset which is comparatively meager, we have achieved a respectable accuracy, something which can be built upon and bettered by boosting number of images collected.

The evolution of path-planning algorithms has been remarkable in recent years. These algorithms are widely used in both industrial and everyday settings. They play a major role in the successful navigation of robots, as well as in games and other applications. Path planning algorithms can provide optimal solutions even in complex, high-dimensional environments. Among all the available path planning algorithms, the most widely used are RRT-Star and its variants, including RRT-Star Connect, RRT-Star Smart, Bidirectional RRT-Star, TG RRT-Star, and others. This paper presents a detailed study of these algorithms, taking into account various metrics for performance evaluation and comparing the results in different environments. The study presents a discussion on the classification of path-planning algorithms based on the environment, the nature of the algorithm, and its completeness. It also delves into the hierarchy of RRT-based algorithms to comprehend the improvements made to the algorithms and the important characteristics of each algorithm that is being followed.