## 6 Traditional and DL Approaches for Object Detection in AVs
### DL Approaches for Object Detection in AVs
#### 6.2.3 DL Approaches for Road Lane Detection
Specifically designed for bad weather, the approach gives priority to the best feature selection from video frames in order to counteract rain-induced blurriness, leading to adequate classification precision even in difficult weather circumstances. In other research [166], two different models for AV lane prediction in highway traffic situations are presented. The study's main focus is on the use of AI for lane prediction using a sizable dataset from NGSIM. To showcase the effectiveness of these models, two distinct subsets with 5000 car samples each were used. The strategy employed a range of classifiers in the Identification Learner and different methods in Neural Net Fitting, emphasizing the methodology's importance in accomplishing good lane prediction without diving into particular accuracy measures. Due to different illumination circumstances, global thresholds for lane marker identification frequently produce disappointing outcomes. An efficient lane departure (LD) technique is presented, enabling lane marker identification that may be used in daylight highway scenarios as well as tunnel situations with artificial lighting. In order to obtain lane features, the usual LD technique begins by performing pre-processing to eradicate perspective-related distortion and isolate an ROI. In order to identify suitable lane markers using color, shape, alignment, or geometrical measurements using the road scenario data, two categorization methods, model-based and feature-based, are used [17]. To minimize false positives, prospective lane markers are further refined and validated using curved or linear lane fitting. This helps with features like adaptive speed control, automatic lane focusing, and lane departure alert. LD devices may not be as successful due to a number of issues, such as road obstructions, ambient lighting sources, and noisy weather. To address these issues, a shadow-resistant methodology that makes use of Fourier transformation and the maximally stable extreme region (MSER) approach in the blue color stream is used as a reference [167]. The research findings [168] suggest that the minimal safe separation between the self-absorbed car and the vehicle should be used to estimate the extraction of ROI. According to the study, a car traveling at 110 km/h may cover a distance of 35 m with a height of just 150 pixels. Usually, a predetermined model or set of features is used to construct lane markers using the ROI. In [169], the authors modified YOLOv3 and presented a brand-new BGRU-Lane (BGRU-L) model; the method integrates spatial distribution with visual data. High accuracy (90.28 mAP) and real-time detection speed (40.20 fps) are achieved in difficult settings through integration utilizing the Dempster-Shafer algorithm, as demonstrated by datasets from KIT, Toyota Technological Institute, and Euro Truck Simulator 2. The dynamic environment of research and innovation in the field of road lane detection for autonomous cars is shown by our thorough analysis of the literature. The abundance of information and developing techniques indicates the ongoing commitment to increasing the potential of self-governing systems.