Insights

The traditional ‘car-landscape’ is changing, and it is changing fast. Between the efforts being made to decarbonise transport, together with the electrification of vehicles, and in light of the COVID-19 ‘Work-From-Home’ efforts, we are experiencing first-hand how a change to the reliance on motor-vehicle usage is taking place. This in juxtaposition with the opportunities Artificial Intelligence (“AI”) provides, we are creating, or attempting to create an environment where transport is safer, efficient, and most importantly, cleaner.

The change in landscape was pushed into acceleration in response to President Biden’s plan to replace the US government’s fleet of 645,000 cars and trucks, (that according to the U.S. General Services Administration consumed 1420 million litres of petrol and diesel fuel over 7.2 billion kilometres), with electric vehicles assembled in the United States. This propelled the car manufacturer Ford, who at the time was not producing any electric vehicles, to commit to go all electric by 2030 and most recently announced an $11.4bn investment in electric vehicle plants. This was also matched by other non-U.S. car manufactures such as Volvo, who announced a new commitment to become a fully electric car company by 2030 and therefore phasing out combustion engines as well as hybrids.

Whilst these efforts are extremely promising, the effort to decarbonise transport must go beyond the electrification of the automotive industry. The most obvious parallel need is to ensure that the infrastructure behind the electric cars is supported by clean and efficient means of charging – wherein AI is also improving on the battery and charging management of electric cars – but beyond that, there must also be a concerted effort and focus on reducing the reliance on private transport.

According to the National Statistics Office (Malta), there were 408,205 licenced motor vehicles at the end of Q2 of 2021, which is nearly one vehicle per adult citizen in the Maltese Islands. Legitimate incentives and efficient infrastructures must be implemented to provide citizens with better alternatives to driving, walking, cycling, micro-mobility and public transport, as the most obvious alternatives. AI and data analytics can help in transportation planning by providing an adequate infrastructure for pedestrians and cyclists for them to safely travel from one destination to the other. It can also be beneficial for certain tracking of pedestrians and cyclists’ paths to be done in order to minimise accidents. This will not only help in infrastructure planning but will also help save our planet with less emissions, enabling us to use time more productively and make the best use of limited resources. AI can also be used as an optimisation engine to predict and match the demand and supply for public transportation routes, resulting in better planning.

Technology has, throughout history, and now more than ever, provided solutions in a wide array of industries and sectors, consequently creating a shift in our corporate culture and lifestyle. Many companies have quickly adapted to work-from-home schedules and are now making plans to embrace policies that offer remote working possibilities to most of their workforce, as well as offering geographical independence. For example, Salesforce and Spotify are not only allowing employees to continue working from home (either on a permanent of flexible basis) but to also choose their geographic location^[1].

Remote working and online services (both public and private) also have the potential to change our traditional reliance on motor-vehicle use. Creating and maintaining a well-established digital infrastructure will undoubtedly contribute to the efficient functioning of the public sector and hopefully reduce the need for physical presence. This may be achieved through the development of a blockchain based digital government that can be leveraged to support and streamline an array of government and public sector applications and processes across an array of relevant departments and ministries including the registration of ownership (movables and immovable), identity management, healthcare, product liability, energy consumption, marine conservation and many more. With the adoption of blockchain technology, individuals, businesses, and governments can share resources and increase efficiency and trust, while simultaneously reducing fraud, waste, and abuse. We must appreciate that the solution to our transport issues may be solved (at least partly) through the digitalization and adoption of technology for the creation of sustainable and efficient cities through the services they provide.

This new industrial revolution has made the transition to working from home a possible reality. Driven by endless connectivity, improved communication tools, digital advanced technologies such as AI, big data, internet of things and blockchain, the way societies and economies function are changing rapidly. In juxtaposition with these advancements, evolving legal frameworks are also contributing to the transition. For example, the General Data Protection Regulations (“the GDPR”) offer significant protection for natural persons in relation to the processing of personal data; guidelines issued by the European Data Protection Supervisor (“EDPS”) provide guidance on privacy related issues in this new environment; and the E-Commerce Act which implements the E-Commerce Directive (Directive 2000/31/EC) into Maltese law, allows contracts signed by the parties on hard copy to be scanned and sent via electronic means to have legal effect; and the eIDAS Regulation^[2] stipulates, as a core principle, that an electronic signature, irrespective of its type, should not be denied legal effect.

The first solution is using technology to reduce traffic congestion while simultaneously building sustainable cities where you don’t have to drive in the first place. Nonetheless, in instances where driving is indeed necessary there are technological solutions which can have a direct impact on transport. AI has revolutionised technologies in transport by taking over certain driving functions, such as for parking, whilst other technologies intend to completely replace the driver. A self-driving electric shuttle by an American company is being powered by IBM’s Watson and its Internet of Things database to analyse the surrounding traffic and make decisions based on that data. Its cognitive computing which includes myriad sensors and systems create an unparalleled opportunity for its customers.

A similar kind of self-driving shuttles were also seen in the recent Tokyo Olympics by Toyota, known as the “e-Palette”. While controversy arose (following the shuttle’s collision with an athlete) as to the potential concerns with regards to the limitations of autonomous driving technology, it is important to point out that the vehicle was not driving autonomously but was under manual control by a human operator at the time of the incident. Apart from this, however, AI algorithms are also used for ridesharing platforms (from matching riders and drivers to route optimisation). In recent years, an AI frontier which was mentioned is the creation of the ‘Defense Department’s Advanced Technology Arm (DARPA)’, which calls for lifelong learning machines which would allow machines to reason and think like a human in the ‘fly-through’ experience of cars.

The EU has, in recent years, placed greater focus on the application of AI in road transport with the policy initiatives on three interlinked areas: (1) revision of existing and discussion on possible future initiatives related to road transport infrastructure; (2) initiatives related to AI applications in road transport, e.g. autonomous vehicles, and (3) horizontal initiatives related to AI technologies in general^[3]. Similarly, the Commission’s strategy ‘On the road to automated mobility: An EU strategy for mobility of the future’^[4] is the overall EU vision. The European Parliament has also been indicating that a key sector for AI is transport and thus its rules shall be harmonised to enhance the cross-border development of connected and autonomous vehicles (CAVs). Nevertheless, there are various existing gaps and barriers to developing and deploying AI in road transport, namely: the quality of road infrastructure and connectivity; gaps in EU’s legal and policy framework both with respect to the environment and infrastructure; and effective enforcement and application of EU rules on road safety.

Ultimately, the main objective of this article is to reiterate the importance of technology in the quest for a solution and to ensure that whilst we adjust to and embrace the idea that the internal combustion engine vehicle will eventually be replaced by the electric car, we need to complement this change with a whole set of other technological and cultural solutions— each contributing to decarbonisation of transportation in general, as well as a gradual shift away from the over-reliance on personal vehicle use to a more sustainable way of living.

The author would like to thank Michela Galea, student intern at Ganado Advocates, for her invaluable help in drafting this article.

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^[1] This is to a certain extent reflecting in the Digital Nomad Visa the Maltese government is currently offering, however, discussion on such falls outside the scope of this discussion.

^[2] Regulation (EU) No 910/2014 of the European Parliament and of the Council of 23 July 2014 on electronic identification and trust services for electronic transactions in the internal market and repealing Directive 1999/93/EC OJ L 257 < EUR-Lex – 32014R0910 – EN – EUR-Lex (europa.eu)>

^[3] European Parliament, ‘Artificial intelligence in road transport’, Cost of non-Europe report, European Parliamentary Research Service, January 2021.

^[4] European Commission, ‘On the road to automated mobility: An EU Strategy for mobility for the future’, <EUR-Lex – 52018DC0283 – EN – EUR-Lex (europa.eu)> COM(2018) 283 final.