… components focuses only on the ML components themselves, without consideration of the
closed-loop behavior of the system. For autonomous driving … of a typical autonomous driving …

… sensors, as well as the vehicle environment. We use … autonomous vehicle system, which
capture both component-level and systemlevel behaviors. Additionally, we present three driving …

… machine learning techniques in order to enhance dependability and safety of machine learning
… and user experience aspects of machine learning components in autonomous vehicles. …

… Autonomous vehicles have to deal with dynamic, nonstationary and highly unpredictable …
autonomous driving: Capture uncertainty in the deep neural network for lidar 3d vehicle …

… producing incorrect output is a FuSa issue, but in the case of an ML component, there can
… FuSa of machine learning components and investigate what aspects of ML components we …

… and deep learning technologies used in autonomous driving, … -art deep learning and AI
methods applied to self-driving cars. … expensive hardware components in a self-driving vehicle. …

… The driving task elements of self-driving vehicles that can benefit from ML can be broadly
categorized into the following four major components (as shown in Figure 8). 1) Perception: …

… autonomous vehicles (AVs). The AutoML enables end-to-end machine learning from vehicle
… characteristics, which integrates three main components of: unsupervised risk identification …

… is to augment learning with two trainable components, as … in the standard manner for the
component in question. The RL-… Thus, every input being passed to the NN-based component …

… adoption of Machine learning (ML) components in many automated systems covering almost
… ML-based components in their products, and the use of such components has emerged in …