Autonomous Vehicles (AVs) equipped with camera systems have emerged as a pivotal solution for smart urban mobility. The escalating demand for AVs emphasizes the need to prioritize driving safety, especially in challenging weather conditions like heavy rain. In this context, the accurate perception of environmental features, notably lane markers, becomes imperative for effective autonomous navigation. Severe weather can lead to camera image degradation, including blur and loss of details, impacting the accuracy of subsequent image processing. Despite the prevalence of camera-based methods, sensitivity to environmental noise, such as rain streaks, poses a challenge, necessitating preprocessing mechanisms like rain removal to enhance lane detection accuracy. This chapter focuses on the development of a vision-based algorithm dedicated to detecting and tracking lane markers, coupled with an efficient rain streak removal algorithm. A progressive approach to lane detection on city roads is presented, incorporating sliding windows and Kalman filter methodologies into a model-based method. Integration of the Kalman filter has yielded a notable improvement in video processing speeds, from 1.67 to 2.72 frames/s, enhancing overall operational efficiency. Furthermore, a novel neural network structure, amalgamating convolutional neural networks (CNNs) and long short-term memory (LSTM), is introduced for rain streak removal before performing lane marker detection. Comparative analysis against existing methods demonstrates an average 2.3% improvement in peak signal-to-noise ratio (PSNR) for rain removal and an 8% enhancement in Google Vision test results.

Autonomous vehicles (AV) are one of the greatest technological advancements of this decade and a giant leap in the transportation industry and mobile robotics. Autonomous vehicles face several major challenges in achieving higher levels of autonomy. One of these is to find a fast and reliable algorithm to process the sensor data so that the simultaneous localization and mapping (SLAM) algorithms run in real-time to achieve autonomous navigation. The major limitation of the SLAM algorithm, especially while building a map is to have static environmental features, i.e. without any dynamic or moving objects. To achieve this, our paper introduces a novel algorithm to remove dynamic objects from point cloud data. The algorithm focuses on identifying and removing dynamic objects from sensor data, thereby creating a static scene suitable for traditional SLAM algorithms. Simulations conducted on the benchmark dataset demonstrate the algorithm's efficacy in successfully eliminating dynamic objects and reconstructing a stable static scene.