Laura Del Vecchio
© kinwun @ stock.adobe.com
Automobile Environmental Impact
According to recent research performed by IEA in 2020, the transportation industry accounts for 16.2% of total greenhouse gas emissions worldwide, of which 11.9% comes from road transport alone. Despite the environmental contributions of vehicles powered with alternatives to fossil fuel (e.g., battery-electric and solar-powered vehicles), many experts agree that CO2 gas emissions will increase, especially considering that the global fleet of vehicles is expected to exceed two billion by 2030.
Initiatives to introduce new kinds of transports that pollute less compared to the fossil-based vehicles are emerging accordingly, as well as different types of fuels that help tackle climate change, such as biofuels [for more information regarding Biofuels, we highly recommend you reading the Biofuels editorial piece]. Yet, the adoption levels of these vehicles depend highly on the final price and decision-making processes to regulate the introduction of new automobile technologies. Also, with the ongoing economic crisis produced by Covid-19, investments in vehicle sales focused on more ecological alternatives are projected to face substantial hindrances as these have a considerably higher final price than current fossil fuel vehicles.
For an effective application of a greener automotive industry, a considerable shift is needed. This comprises an active contribution from governments and institutions to demand from automobile industries an adaptation of their practices to the current environmental paradigm, as well as an infrastructural investment from all forms of road transport. This is no easy task. However, as in many other sectors, digitalization is bringing new opportunities that are adding value to monitoring, prediction, and analysis of all variables behind vehicles and the transportation supply chain.
IoT technological applications are starting to bridge the communication between vehicles and their surroundings, and the use of AI-based software is improving the movement of vehicles and pedestrians altogether. The data gathered by these technologies are helping elaborate strategies to reduce carbon emissions and design plans to promote better urban planning, ranging from expanding lanes, calculating revised fuel consumption patterns, to enabling more crosswalks where needed. In the following sections, we depict the technological solutions involved and disclose some opportunities and challenges we could expect in terms of implementation.
Vehicles Communication and Contributions to Sustainability
Automation, as a multidimensional movement, is expected to bring manifold sustainable mobility solutions, and Self-driving Vehicles are only the tip of the iceberg. Communication protocols, such as the 5G Network, are the basis for the creation of a connected hub that can receive and provide information to lower operational costs and reduce environmental impact. As an effort to prototype transport solutions that can increment the transition from a fossil-based to an environmentally-sound automobile sector, Ericsson Research has recently joined Scania and the Royal Institute of Technology (KTH) in the Integrated Transport Research Lab program to test the behavior and outcomes of the 5G Network, Self-driving Vehicles, and many other emerging techs.
The project is taking place in the suburb of Kista, Stockholm. The results are leading experts to better conclusions of the required specs that will ultimately support an eco-friendly mobility system as well as safer transportation modes to both pedestrians and drivers. The scenarios run by experts range between three categories; driver assistance (trucks and buses driven by humans that receive constant recommendations from an embedded software), semi-automation (all types of autonomous vehicles overseen by off-board human operatives), and full automation (fully autonomous vehicles conducted by software).
The major enabler for these tests is the implementation of several communication protocols; vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communications. In the first, vehicles are used as a network of independent information nodes that gather and share information. The collected data is shared among nearby vehicles and other static roadside systems while also being utilized by Machine Learning Data Analytics that sends information back to the vehicles and Smart Traffic Lights to reduce traffic and bypass congested areas. The wireless link between vehicles increases situational awareness, allowing vehicles to accelerate and brake in unison and radar detectors to analyze potential danger up ahead and avoid accidents as well as reducing transportation carbon footprint by reducing unnecessary braking or speeding, similar to vehicle platooning (a caravan in which vehicles move in-line close to each other, adjusting braking and speedup). The vehicle-to-infrastructure (V2I) system, on the other hand, takes advantage of key components ranging from Smart Traffic Lights, warning communication systems, and a Integrated Operations Center responsible for monitoring both driver behavior and proper vehicle mechanical operation. V2I counts with an onboard vehicle router used to send and receive information wirelessly and process the data gathered from the sensors.
The combination of the aforementioned communication protocols enables a next-generation wireless interaction commonly known as a vehicle-to-everything (V2X) communication network. Currently, V2X is built upon already existing communication technologies, such as Dedicated Short-range Communication (DSRC). This technology, for decades, has been the only enabling wireless communication to automotive coordination, which, for many reasons, suffered from considerable downsides, including low data rate, limited coverage and quality-of-service (QoS) guarantees, and information access delay. With time, the implementation of Cellular Vehicular Communication (CVC), such as the LTE network or 4G LTE —which stands for 4G Long Term Evolution—, enabled more reliable vehicle interactions. The use of the LTE network for V2X communication improved communication between vehicles and infrastructure points, solving some of the aforementioned data exchange obstacles.
Later, the implementation of a faster 4G cellular communication allowed interaction for vehicle-to-network (V2N) communication, while enhancing accountability for the vehicle-to-vehicle and vehicle-to-infrastructure communications. Even though it is at trial stages, 5G networks will evolve from existing 4G infrastructure instead of formulating an entirely new network from scratch. For example, by taking advantage of one of the most important components of 4G LTE networks, the Evolved Packet System (EPS), would assign quality-of-service (QoS) parameters to classify its bandwidth, latency, priority, and packet loss. In other words, the EPS provides a more defined and transparent network, which is crucial for establishing 5G networks, and therefore, improved V2X communication.
With the expectation of increasing the number of autonomous vehicles circulating worldwide, also considering the rapid spread of urbanized areas, more investment in spectral (e.g., Wi-Fi) and hardware resources (e.g., cameras, sensors) are needed for deploying 5G networks and improving V2X communication. Alongside automated vehicles, other services will impose considerable challenges for V2X infrastructures, such as Multimodal Acoustic Trap Display (MATD). This will dramatically stress the capacity limits of current wireless networks, such as coverage, energy efficiency, networking, security, among others.
As driverless autonomous vehicles become a reality, the need for a connected infrastructure increases. The demand for a unified, connected system will arise as soon as autonomous and non-autonomous vehicles start to share the roads. It would require monitoring to ensure the environment can accommodate the unpredictability of interactions on a mixed traffic road. Together with Deep Learning, the traffic data could be used to elaborate predictions of particular zones and design plans to improve urban planning, ranging from expanding lanes to enabling more crosswalks where needed and benefiting cities by reducing CO2 emissions.
In terms of public transportation, V2X networks could improve commuting by taking advantage of demand prediction data harnessed from Drone Monitoring and Low Earth Orbit Satellites to expand coverage and enhance communication. Traffic authorities could benefit from using data provided by these tools, for example, monitoring specific congested areas, vehicle speed, and intended routes, and promoting better urban traffic flow and optimized traffic signals. The employment of Edge Computing could make interconnected devices in V2X communication perform faster computation and improved decision-making. This could lead to more fluid traffic, with autonomous vehicles circulating with no need for braking. It would not mean just reducing the quantity and intensity of traffic jams; mobility itself would be reimagined, possibly allowing citizens to live farther away from cities, as distances would not affect individual routines.
The emerging urban transportation solutions we mentioned above, will raise specific requirements on the network connection, for instance, data exchange faster communication and information interplay for moving vehicles and surrounding infrastructure. The current 3G and 4G networks will not disappear but will likely co-exist with other network services to optimize transportation. For example, instead of only relying on a 5G network to enable a fully connected environment, for example, Emergency Citizen Responder tools could still rely on 3G and 4G networks to not saturate 5G systems. This strategy will help secure network resources even in high network traffic scenarios, allowing 5G networks to become the foundation to build alternative transport solutions.
As this connected scenario is based on gathered and processed data, future data storage networks must adequately protect such sensitive information extracted from vehicles, passengers, and entire road systems. A privacy breach of data could allow a hacker to track specific individuals or cause general mayhem if they were to create inexistent hazards or dangerous road warnings. Future network communications will also have to keep up with the velocity of data being exchanged. For example, weaknesses showed by existing Smart Traffic Lights range from unencrypted wireless connections, default usernames and passwords that can be encountered online, and a debugging port that is exposed to attacks. These issues could be solved by applying intricately encrypted systems, such as Adversarial Machine Learning, which would make the network more reliable besides refining it to the circumstances of each city.