The rising incidence of traffic accidents can be attributed to multiple factors, with one notable contributor being human behavior. Each driver is required to obtain vehicle insurance to mitigate potential damages resulting from accidents. In the event of an accident, insurance companies rely on capturing comprehensive evidence of the damages. Presently, insurers often request video footage to serve as evidence, enabling them to assess the extent of the damage and calculate appropriate insurance rates for the driver. Video footage, in this context, refers to recordings from the vehicle’s
dashboard camera. Additionally, insurers can use this footage to
ascertain whether the driver has committed any traffic violations, such as speeding, disregarding traffic lights, tailgating, unsafe lane changes, etc. Penalties may be imposed on drivers found to have committed violations based on the evidence gathered from the video footage. Ego vehicle speed prediction involves determining a driver's vehicle speed based on video footage provided by the driver. Car insurance companies can use dashboard camera video to assess whether a driver exceeds the speed limit. In the context of traffic light classification, the system is designed to detect the presence of a traffic light and ascertain its current state, whether it is
displaying a red, yellow, or green signal. This classification system can also be employed to identify instances where a driver may have violated traffic rules by running a red light.
In this study, the Ego-Vehicle Speed Prediction employs Convolutional Neural Networks (CNNs) for both training and testing. Several techniques are integrated to enhance the prediction accuracy. Specifically, Histogram Equalization is employed to refine Optical Flow estimation. Furthermore, within the CNN architecture, a CBAM (Convolutional Block Attention Module) is incorporated to amplify object visibility and recognition within the images. The primary datasets leveraged in this study are the KITTI dataset and the Comma.AI dataset, both of which provide speed measurements
in meters per second (m/s). To evaluate the performance of the
Ego-Vehicle Speed Prediction model, metrics such as sMAPE, MAE, and RMSE are utilized. Beyond Ego-Vehicle Speed Prediction, the study also delves into Traffic Light Classification. For this task, datasets from LISA Traffic Light and Udacity Traffic Light are employed. To simplify the classification process, the classes within these datasets are categorized into three primary labels:"go", "warning", and "stop". For the actual classification, YOLOv5 and
YOLOv6 architectures are utilized, aiming to accurately identify and classify traffic lights based on the aforementioned simplified categories. To evaluate and compare the performance of YOLOv5 and YOLOv6 in terms of accuracy, the metrics MAP@0.5 and MAP@0.5:0.95 are employed. These metrics provide insights into the models abilities to detect and classify objects within images, with varying levels of overlap or intersection over union (IoU) thresholds.

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