# Enabling Technologies for Urban Smart Mobility: Recent Trends, Opportunities and Challenges
## 5 Enabling Technologies to Support Smart Mobility
### Role of Enabling Technologies in Smart Mobility Services and Applications
#### 5.2.3 Role in Autonomous Vehicles  
The adoption of blockchain in autonomous vehicles already has several proposals in the literature. One of its applications is to support a system that stores events that happened in the autonomous vehicle and that can be useful when there is an accident between vehicles or involving a human being [139]. In these situations, responsibility must be decided on the basis of a system that makes it possible to understand the various events that have taken place in the autonomous vehicle. Another application of the blockchain in AVs is related to car-sharing or car-pooling and the guarantee of trust that is intended to exist among everyone involved in this type of system, which is also directly linked to the concept of MaaS [140]. Blockchain and smart contracts are also being used to ensure the authenticity and integrity of the firmware update process by manufacturers of the systems that support AVs, which are increasingly subject to attacks [141].  
Geospatial technologies aim at tracking and tracing vehicles and pedestrians, thus providing a range of mobility services. In [142], a system is presented that tracks and monitors the autonomous vehicle in real time using a Frequency Modulated Continuous Wave (FMCW) radar. In [143], the authors propose a model to address the accuracy of tracking and vehicle stability. The model aims to perform the predictive control that intends to monitor any deviations in the trajectory in order to keep the vehicle stable, even at high speed.  
One of the relevant aspects with regard to AVs is the choice of optimal trajectory. This choice, in [144], is made by the authors using Big Data mining and the analysis of real accident data as well as real-time data from connected vehicles. In [145], the authors propose an autonomous accident detection system in real time based on computational intelligence techniques. The study uses Big Data processing methodologies to analyze 2015 traffic flow data from Istanbul collected from different sensors, which are also intrinsically related to how autonomous vehicles are connected to the Internet of Things.  
Autonomous vehicles represent the future, in which vehicles are expected to be connected and electric. This evolution is directly linked to climate and environmental sustainability, taking into account an EU directive that requires a 40% reduction in CO2 by 2030 compared to 1990. The reduction in CO2 emissions from vehicles goes largely through the transition for electric vehicles [146].  
Vision-based artificial intelligence in autonomous vehicles allows a set of features including the detection of obstacles as well as mechanisms to avoid them [147]. In [148], the authors propose a way to increase the processing power to estimate the distance to obstacles using Compute Unified Device Architecture (CUDA) along with Believe Propagation Algorithm (BPA). The use of AI for in-vehicle vision systems to power Advanced Driving Assistant Systems (ADAS) is explored in [149].  
Table 7 summarizes, for an easier understanding, the studies that apply each one of the technologies to the smart mobility service or application.