# Innovation Potentials and Pathways Merging AI, CPS, and IoT
## 5 Case Analysis Autonomous Truck Driving and Maintenance
### Description Status Quo  
According to many regulatory demands and authorities, a human person will be on board due to security reasons at least in public traffic environments for the near future. This allows for the observation, that in the future humans will not be employed for their 'know-how' but their 'know-why'. The competence to actively gear and steer e.g., the truck will be implemented by a technology application--whereas the driver is supposed to understand the know-why of all systems and especially when to interrupt the automated system (therefore also: 'know-when'). Human interaction towards AI applications and automation as human-computer interaction (HCI, see [69; 70; 71; 72; 73]) can be characterized by three hurdles or areas of resistance. Once an area is overcome, acceptance may settle in. This is depicted in the following Figure 4.  
Figure 4: Human acceptance resistance model (from [11]). The three signified hurdle areas are connected to three increasing AI functional areas and induce also an increasing level of resistance throughout this development in line with an increasing level of personal intrusion (PI, _x_-axis): _AI Competences:_ Automation and AI applications are increasingly acquiring competences in specified fields (compare Table 1), from playing chess or poker to forecasting market demand in value chains. As separate competences, these are new for humans to get accustomed to but are comparatively less frightening and therefore the resistance level towards them is relatively low. For logistics, this may include for example the automated gearbox in truck driving, automated routing and navigation systems as well as automated intralogistics applications systems such as picking and warehouse transportation systems. These systems have in common that usually any final decision e.g., regarding the travelled street in reality are still taken by humans--and in many cases as a lot of people know personally the AI suggestions for example from navigation systems are not followed through by humans, an obvious sign of resistance. _AI Decisions:_ Further, AI applications are suggesting and applying single decisions, which usually rises greater anxiety and resistance levels with humans. This happens for example in cruise control applications in cars and trucks-with three development distinctions: maintaining constant speed, maintaining constant distance to front-vehicle and finally variation of speed according to anticipated terrain features. In such cases, the automated device is taking a single or a row of decisions within a limited area of action (e.g., vehicle speed, vehicle gear). Such developments have already happened in the past for example for car and truck motor management (increasingly automated) or also in the leisure area for smartphone and social media applications. In these cases, humans are accepting automated applications without any security or fraud mistrust, at least not on a day-to-day basis (only incident-based e.g., with data fraud scandals or leaks). Understandably, this sort of AI application is usually also raising higher levels of rejection among humans, requiring a longer period of adaption before acceptance can settle in (e.g., [74]). _AI Autonomy:_ Finally, AI applications are taking a multitude of differentiated decisions, leading to autonomous behavior as e.g., in actively steering cars and trucks for longer periods and with interaction towards other participants in road traffic. In these cases, humans usually take over a passive controlling role (supervision; [75]). These applications are at the doorstep to industrial and real-world application, in production (autonomously moving robots with human interaction), traffic (autonomous cars and trucks) as well as health care (surgery as well as care robotics).