Knowledge of the tip contact force in continuum robots, which are often used as medical instruments, is critical for clinical applications. It enhances the interventionalist's decision-making, navigation efficiency, and procedural safety. However, accurately determining the tip contact force in conventionally sized instruments remains challenging. This study introduces a learning-based method for estimating the external contact force at the tip of a continuum robot. By leveraging curvature and bending angle data from a multi-core fiber equipped with fiber Bragg gratings (FBGs) embedded inside the Nitinol tube, the method maps these inputs to the corresponding tip force in 3D. Experiments conducted on an FBG-embedded Nitinol rod validate the feasibility of the proposed method, yielding Mean Squared Error (MSE), Mean Absolute Error (MAE), and Root Mean Squared Error (RMSE) values of 20.9$\left(m N^{2}\right), 2.7(m N)$, and$4.6(m N)$, respectively, which represent a 26 % improvement compared to the learning-based vision methodology.

Nowadays, Advanced Driver Assistance Systems (ADAS) play important roles in improving the safety of driving. ADASs along with new safety standards are now essential to improve the safety of automated driving. These standards differ from the safety standards in other industries because an autonomous vehicle rides in a very intricate stochastic environment where many details should be considered to maintain the safety. From one point of view, autonomous car failures are divided into two categories: hardware and software failures. According to the literature, if the reason of fault belongs to hardware, the troubleshooting will relatively be easier than software failure. In other words, if the fault is the subset of software, that would be more challenging task to detect potential fault and recover from the failure. In this paper, we propose a new approach to detect the software failures, which may be unpredictable using a typical safety protocol. A predictive machine learning method is developed to improve decision making process in order to avert the potential accidents occurred by software defects. The proposed method evaluates detected obstacle in the span of a specified number of frames to discern whether objects are detected correctly or not. Hence, it provides extra time to choose the suitable action before it reaches the danger, which includes lane changes, cut-in maneuvers, stopping the car, accelerating the car, slowing down, and allowing vehicles to pass. Results show that the new approach improve the safety by increasing the precision of failure detections.