Artificial Intelligence (AI) contributes significantly to the development of autonomous vehicles in an unmatched way. This paper outlines techniques and algorithms for the implementation of Intelligent Autonomous vehicles (IAV) leveraging AI algorithms for traffic perception, decision-making and control in autonomous vehicles through merging traffic scenario detection, traffic lane detection, semantic segmentation, pedestrian detection, and traffic sign classification and detection. The modern computer vision and deep neural networks-based algorithms enable the real-time analysis of different vehicle data through artificial intelligence. The vehicle dynamics are constituted through AI in vehicle control systems for increased safety and efficiency to ensure that they are optimized with time. In addition, the paper will also discuss challenges and possible future directions, underscore how AI has the potential of driving autonomous vehicles towards safer and more reliable as well as intelligent transportation systems. This is the hope of the future whereby mobility is intelligent, sustainable, and accessible with the combination of AI with autonomous vehicles.
Over the past decade, researchers have suggested many methods to find anomalies. However, none of the studies has applied frame reconstruction with Object Tracking (OT) to detect anomalies. Therefore, this study focuses on road accident detection using a combination of OT and U-Net associated with variants such as skip, skip residual and attention connections. The U-Net algorithm is developed for reconstructing the images using the UFC-Crime dataset. Furthermore, YOLOV4 and DeepSort are used for object detection and tracking within the frames. Finally, the Mahalanobis distance and the reconstruction error (RCE) are determined using a Kalman filter and the U-Net model.
Additive manufacturing techniques have been the focus of studies and technological advances in recent years, obtaining the capability to fabricate pieces with complex geometries easily, rapid and with high precision, allowing the use of different materials, the appearance of new techniques, and a range of applications beyond prototyping. However, Additive Manufacturing techniques are still affected by some deficiencies and challenges such as the absence of sensing and control during the fabrication process that would result in a more reliable process and printed part. This paper shows the development of an inference process using probabilistic graphical models, in order to track the motion of the extrusion nozzle during the printing process using linear encoders
Automated Driving requires fusing information from multitude of sensors such as cameras, radars, lidars mounted around car to handle various driving scenarios e.g. highway, parking, urban driving and traffic jam. Fusion also enables better functional safety by handling challenging scenarios such as weather conditions, time of day, occlusion etc. The paper gives an overview of the popular fusion techniques namely Kalman filters and its variation e.g. Extended Kalman filters and Unscented Kalman filters. The paper proposes choice of fusing techniques for given sensor configuration and its model parameters. The second part of paper focuses on efficient solution for series production using embedded platform using Texas Instrument's TDAx Automotive SoC. The performance is benchmarked separately for "predict" and "update" phases on for different sensor modalities. For typical L3/L4 automated driving consisting of multiple cameras, radars and lidars, fusion can supported in real time by single DSP using proposed techniques enabling cost optimized solution.