**5.4 Driver self-regulation**

Self-regulation and/or compensatory behaviour of older adults is defined in relation to the tendency of older adults to minimise driving under conditions that are threatening and/or cause discomfort and conversely, to restrict their driving to conditions perceived as safe and/or comfortable [44].

Compensatory behaviour of older adults includes avoiding driving in the following situations/conditions:


As stated in the Eldersafe Report (2016), older road users need to be aware, acknowledge and have insight into their functional impairments in order to selfregulate [47].

### **5.5 Driving cessation**

Health deterioration is the primary trigger/key determinant for driving cessation among older adults [48]. Medical conditions either (1) impact the fitness to drive of older drivers and/or (2) an older person's perceived fitness to drive (i.e. attitude, confidence levels, etc.). Several medical conditions and associated impairments are more prevalent in the older adult population and are, therefore, associated with ageing. These medical conditions can potentially impact the crash risk of older road users [49]. Specifically, a systematic review of the literature by Marshall identified specific conditions including: alcohol abuse and dependence, cardiovascular disease, cerebrovascular disease/TBI, depression, dementia, diabetes mellitus, epilepsy, use of certain medications, musculoskeletal disorders, schizophrenia, obstructive sleep apnoea, and vision disorders [50].

**25**

*Ethical Issues in the New Digital Era: The Case of Assisting Driving*

The path to automated/driverless cars began before 2000 with the introduction of cruise control and antilock brakes. Since 2000, new safety features such as electronic stability control, blind spot detection and collision and lane shift warnings have become available in vehicles. Further, since 2016, automation has moved towards partial autonomy, with features that enable drivers to stay in lane, along

Automated driving systems are defined as systems that control longitudinal and lateral motions of the vehicle at the same time [51]. Self-driving cars use a combination of sensors, cameras, radar and artificial intelligence (AI) to travel between destinations without a human operator. The Society of Automotive Engineers (SAE)

In addition, BASt [53] and the National Highway Traffic Safety Administration

Many automotive companies are developing and/or testing driverless cars. This includes Audi, BMW, Ford, General Motors, Tesla, Volkswagen and Volvo. Solutions are also being advanced by Google and Uber. As of 2019, a number of car manufacturers have reached Level 3 [54]. This level involves an automated driving system (ADS) which can perform all driving tasks under certain circumstances, such as parking the car. In these circumstances, the human driver must be ready to re-take control and is still required to be the main driver of the vehicle [54]. According to the Vienna Convention on Road Traffic (2017), as of 2017, automated driving technologies will be explicitly allowed in traffic, provided that these technologies are in conformity with the United Nations vehicle regulations or can be overridden or switched off by

As noted earlier, technology innovation influences societal values and raises ethical questions. As posed by BMVI, how much dependence on technologically complex systems will the public accept to achieve, in return for increased safety, mobility and convenience [56]? In relation to the advancement of assisted driving solutions, Gasser distinguishes four clusters of issues, (1) legal issues, (2) functional safety issues, (3) societal issues (including issues of user acceptability) and (4) human machine interaction (HMI) issues [53]. A recent literature review on the ethical, legal and social implications of the development, implementation, and maturation of connected and autonomous vehicles (CATV) in the United States groups the issues into the following themes: privacy, security, licensing, insurance and liability, infrastructure and mixed automation environment, economic impact, workforce disruption, system failure/takeover, safety algorithm and programming

with adaptive cruise control technology, and the ability to self-park.

has defined six levels of driving assistance technology (level 0–5) [52].

*DOI: http://dx.doi.org/10.5772/intechopen.88371*

**6. Self-driving cars and ethical issues**

• No automation

• Driver assistance

• Partial automation

• High automation

• Full automation

the driver [55].

• Conditional automation

(NHTSA) [13] have defined equivalent standards.

ethics, and environmental impact [57].
