## 1 Introduction  
Automated driving has generated increasing interest in recent decades due to its potential to address some of the most challenging issues faced by people and communities across the world. Road traffic crashes account for 1.35 million deaths a year worldwide, and they are the leading cause of death among children and young adults aged 5 to 29 years [1]. In addition, many people in different parts of the world still lack access to mobility, particularly in rural areas, and the number of older people who are no longer physically capable of driving has been increasing globally at the fastest rate recorded [2]. Another important issue in large metropolitan areas throughout the world is excessive traffic congestion, which is directly related with the growing number of vehicles on the road [2, 3]. These challenges can be addressed by successful implementation of automated driving on public roads. With automated vehicles it is possible to achieve greater road safety, universal access to mobility, and higher transportation efficiency [4]. SAE International proposed different levels for driving automation, which include six levels ranging from no driving automation (level 0) to full driving automation (level 5) [5]. In level 0-2 systems, the driver is expected to respond to any evident vehicle system failure. A level 0 system does not provide any driving automation functions, while a level 1 system supports the driving task by performing either the longitudinal or the lateral motion control. A level 2 system performs both the longitudinal and the lateral motion control when engaged, and the driver is expected to supervise the automation system and take over the driving task whenever necessary to maintain safe driving. In level 3-5 systems, the driver determines whether to engage the automated driving system, and when engaged, the system performs the entire driving task. A level 3 system offers conditional driving automation, which permits the engagement of the automated driving functions only within its operational design domain (ODD). When engaged, the driver is expected to be responsive to a request to intervene and any system failures, and be ready to perform the driving task fallback in a timely manner. Similarly, a level 4 system also permits the automated driving engagement only within its ODD, but the driver is not expected to perform the driving task fallback and becomes a passenger of the vehicle while the system is engaged. Finally, a level 5 system permits the automated driving engagement under all driver-manageable on-road conditions, without limitations on the ODD. Despite the considerable interest and effort devoted to automated driving in the past decades, most experts agree that level 5 systems are still decades away from becoming a reality on public roads [6]. At the moment, most automakers provide level 1 and 2 automation functions in the production vehicles. One of the most widely known level 2 systems is the Tesla Autopilot [7]. It has been stated that Tesla vehicles had logged a total of 3 billion miles with the Autopilot engaged as of February 2020 [8]. Tech companies such as Waymo and Uber have been working on the development of level 4 systems for ride-hailing services that involve a fleet of automated vehicles operating only within its ODD (e.g., specific geographical locations and appropriate weather conditions) [9]. The driving automation functions of automobiles require accurate and reliable perception of the surrounding environment, which is typically achieved by the use of remote sensors, such as radars, cameras, and lidars [10; 11; 12; 13; 14]. However, perception is a very challenging task due to the highly dynamic and complex nature of driving and traffic environment as well as varying lighting and weather conditions that affect the performance of the on-board sensors [15]. Perception errors in automated vehicles have resulted in a number of fatal crash incidents, and here we discuss some of the notable examples.