# Innovation Potentials and Pathways Merging AI, CPS, and IoT
## 2 State of the Art and Recent Advances
### Transportation  
Autonomous road driving for cars and trucks is on the verge of general application as e.g., [PERSON] et al. (2016), [PERSON] et al. (2017) or [PERSON] and [PERSON] (2018) describe. This is connected to the fact that on the one hand an increased number of sensors are employed in vehicles (infrared, radar, laser, lidar, visual cameras etc.). On the other hand, former independent systems are connected and cooperating in order to perform self-sufficiency in driving. For example, the cruise control system was known for many years in trucks and cars, maintaining a constant pre-set speed for the vehicle. This is now coupled with further intelligent applications, e.g., with the GPS navigation and the automated gearbox, allowing vehicles to deploy dynamic cruise control. This is structured in three sub-level steps, today implemented in the trucking sector:  
* In the first generation of cruise control applications, the system steadily maintained a constant, preset speed level. This was only steering the gas and propulsion system of the truck.
* Second, the system was able to follow a preceding vehicle with a present distance length with varying speed, therefore already combing the management of gas, automated gearshift and brake.
* Third is the competence of current dynamic cruise control systems to anticipate the route characteristics via GPS positioning in combination with map material. This allows the system e.g., to decelerate and upshift before downhill passages or accelerate and downshift before uphill road segments. This combines gas, brake, gearshift as well as GPS and navigation capabilities. The driver is furthermore only steering and supervising the comprehensive system.  
This is a small but significant development on the pathway towards automated vehicle driving as described also for cars ([PERSON] et al., 2016; [PERSON] et al., 2016). Finally, this will lead to automated road transportation with the existing truck driver playing only a supervisory role. A step in the middle is the truck platooning system, where trucks \"hook up\" for a specified part of the route (e.g., on motorways) and couple automatically such as a \"train\"; in this situation only the heading truck driver is obliged to steer and control, whereas the following drivers can rest or work in other capacities, only supervising the automated platooning systems in their trucks. The platooning concept also saves significant amounts of fuel due to reduced drag ([PERSON] et al., 2016; [PERSON] et al., 2016; [PERSON] et al., 2016).  
Altogether, the innovation speed is astounding in a long-term perspective: Whereas in 2004 none of 15 started autonomous vehicles made it through a simple 140 miles desert track in the DARPA Grand Challenge contest, already twelve years later in 2016 the first public road delivery by a beer trailer from the start-up company OTTO travelled 120 miles autonomously without major problems ([PERSON] et al., 2016). This lends to the hypothesis that the next 10-15 years will see major and widespread applications of autonomous trucking at least in some countries and with specific application areas, especially on long-haul distances.