A simple internet search reveals several reported cases where the cars have made errors in identifying objects on the road. For instance, a Tesla car mistook an image of a stop sign on a billboard for the real thing and confused the yellow moon with a yellow traffic light.

There have also been numerous recent examples of problems with the “robotaxis” operating in San Francisco. It raises questions about whether the technology that enables vehicles to operate autonomously is ready for the real world.

The driving force behind self-driving vehicles is artificial intelligence (AI), yet current algorithms lack the human-like understanding and reasoning necessary for context when driving. This includes advanced contextual reasoning for interpreting complex visual cues such as obscured objects, and inferring unseen elements in the environment.

Social interaction

Furthermore, these vehicles must be capable of counterfactual reasoning –evaluating hypothetical scenarios and predicting potential outcomes. This is a crucial skill for decision making in dynamic driving situations.

For instance, when an autonomous vehicle (AV) approaches a busy intersection with traffic lights, it must not only obey the current traffic signals but also predict the actions of other road users and consider how those might change under different circumstances.

An example of this scenario is provided by a 2017 accident in which an Uber robotaxi drove through a yellow light in Arizona in 2017 and collided with another car. At the time, there were questions about whether a human driver would have approached the situation differently.

Additionally, social interaction – an area where humans excel and robots falter – is essential. For example, on urban roads with cars parked along both sides, it’s not always clear who has the right of way and we use social skills to negotiate a fair way to proceed.

At roundabouts, it’s common for several cars to arrive at once, making it unclear who has right of way. Again, social skills allow drivers to safely pull onto the roundabout.

To ensure seamless coexistence with AI-driven cars, we urgently need to develop groundbreaking algorithms capable of human-like thinking, social interaction, adaptation to new situations and learning with experience. Such algorithms would enable AI systems to comprehend nuanced human driver behaviour, react to unforeseen road conditions, prioritise decision making that factors in human values, and interact socially with other road users.

As we integrate AI-driven vehicles into existing traffic, the kinds of standards we’ve been using to assess and validate the success of autonomous driving systems will become insufficient. There is a pressing need for new standards and mechanisms to assess the capabilities of these driverless cars.

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Specific uses

These new protocols should provide more rigorous testing and validation methods, ensuring that AI-driven vehicles meet the highest standards of safety, performance and interoperability (where AI systems from different manufacturers can work “understand” and work together). In doing so, they will establish a foundation for a safer, more harmonious traffic environment where driverless and human-driven cars mix.

It would be a mistake to write off fully self-driving cars, even without the developments which are needed. There is still a place for them, albeit not as ubiquitously as the rapid spread of Tesla vehicles might indicate. We’ll initially need them for specific uses such as autonomous shuttles and highway driving. Alternatively, they could be used in special environments with their own dedicated infrastructure.

For instance, autonomous buses could drive a predefined route with a dedicated lane. Autonomous trucks could also have a separate lane on motorways. However, it’s crucial that uses focus on benefiting the entire community, not just a specific – usually wealthy – group in society.

To ensure autonomous vehicles are well integrated on our roads, we’ll need a diverse groups of experts to enter into a dialogue. These include car manufacturers, policymakers, computer scientists, human and social behaviour scientists and engineers and governmental bodies, among others.

They must come together to address the current challenges. This collaboration should aim to create a robust framework that accounts for the complexity and variability of real-world driving scenarios.

It would involve developing industry-wide safety protocols and standards, shaped by input from all people with a stake in the matter and ensuring these standards can evolve as the technology advances.

The collaborative effort would also need to create open channels for sharing data and insights from real-world testing and simulations. It must also foster public trust through transparency and demonstrate the reliability and safety of AI systems in autonomous vehicles.

Author: Prof Saber Fallah, Director of Connected Autonomous Vehicles Lab at the University of Surrey, University of Surrey

This article is republished from The Conversation under a Creative Commons license.

Image Credit: Metamorworks / Shutterstock

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The transportation and logistics industry is a critical component of the global economy. In recent years, companies in this industry have adopted advanced technologies to optimise their operations and reduce costs. One such technology is Artificial Intelligence (AI), which is becoming increasingly popular in predictive maintenance. Predictive maintenance involves using data to identify when equipment is likely to fail, allowing for repairs to be made before a breakdown occurs. This article will explore how AI is used in predictive maintenance for transport and logistics equipment, its benefits to the industry, and its challenges and limitations.

The Importance of Predictive Maintenance in Transport and Logistics

Predictive maintenance is crucial in the transportation and logistics industry. This is because equipment failures can have significant consequences, such as delays, increased costs, and even accidents.

Transport and logistics equipment refers to a wide range of vehicles, machinery, and other assets used in the transportation and logistics industry. This can include trucks, trailers, trains, cargo ships, aeroplanes, forklifts, cranes, and conveyor systems, among others.

These assets play a critical role in moving goods and people from one place to another, and they are subject to wear and tear over time. The failure of one of these assets can cause delays, accidents, and other issues that can impact the efficiency and profitability of a company’s operations.

Traditionally, maintenance has been carried out based on a fixed schedule, regardless of whether or not the equipment requires it. This approach can be costly and inefficient since maintenance is carried out whether or not it is necessary. On the other hand, predictive maintenance involves analysing data from the equipment to determine when maintenance is needed. This approach can help companies save money by reducing the frequency of unnecessary maintenance and minimising the risk of equipment failure.

For example, in the case of trucks and trailers, AI-based predictive maintenance systems can monitor factors such as tire pressure, brake wear, engine performance, and fuel consumption to identify potential issues before they become serious. This can help companies optimise their maintenance schedules, reduce repair costs, and ensure that their fleets are operating safely and efficiently.

In the case of cargo ships, mechanical failures can cause delays in the delivery of goods, resulting in financial losses for both the shipping company and the recipient. AI-based predictive maintenance systems can monitor factors such as engine performance, hull corrosion, and ballast water management to identify potential issues that could impact the vessel’s safety or efficiency. By identifying these issues early, companies can schedule maintenance activities and avoid costly downtime or safety incidents.

In the case of airports, AI-based predictive maintenance systems can monitor factors such as runway surface conditions, aircraft fuelling systems, baggage handling systems, and air traffic control systems to identify potential issues before they impact operations. This can help companies optimise their maintenance schedules, reduce downtime, and ensure that their operations are running smoothly. For commercial aircraft, for example, maintenance issues can cause flight cancellations or delays, resulting in lost revenue and reduced customer satisfaction.

Predictive maintenance using AI can help companies address these issues by analysing data from sensors and other sources to predict when equipment will likely fail. This allows companies to schedule maintenance activities proactively, reducing downtime and the risk of unexpected failures.

The Role of Artificial Intelligence in Predictive Maintenance

AI technology allows machines to learn and make decisions based on data. In the context of predictive maintenance, AI can analyse large amounts of data from sensors and other sources to identify patterns and anomalies that may indicate impending equipment failure. AI algorithms can also be used to predict the remaining useful life of the equipment and recommend maintenance actions based on the data.

There are several ways in which AI can be used in predictive maintenance for transport and logistics equipment. These include:

1. Predicting equipment failures

AI can be used to analyse data from sensors on equipment to identify patterns that may indicate an impending failure. For example, if a sensor detects an increase in vibration or temperature, this could be a sign that the equipment is about to fail. AI algorithms can analyse this data and predict when maintenance is needed before a failure occurs.

2. Recommending maintenance actions

AI algorithms can also recommend specific maintenance actions based on the data. For example, if a sensor detects wear on a particular component, the AI can recommend replacing or repairing the part. This can help companies prioritise maintenance activities and ensure that the most critical components are addressed first.

3. Optimizing maintenance schedules

AI can also be used to optimise maintenance schedules based on the data. This involves analysing the data to determine when maintenance is most likely needed and scheduling it accordingly. This approach can help companies reduce downtime and improve the overall efficiency of their operations.

Benefits of Using AI in Predictive Maintenance for Transport and Logistics Equipment

There are several benefits to using AI in predictive maintenance for transport and logistics equipment. These include:

1. Reduced downtime

By predicting equipment failures before they occur, AI can help companies reduce downtime and minimise the impact of maintenance activities on their operations. This can result in increased productivity and reduced costs.

2. Improved safety

AI can also improve safety by identifying potential equipment failures before they occur. This can help prevent accidents and reduce the risk of injuries to personnel.

3. Increased efficiency

By optimising maintenance schedules and prioritising maintenance activities, AI can help companies improve the overall efficiency of their operations. This can result in increased throughput and reduced costs.

4. Cost savings

By reducing downtime, improving safety, and increasing efficiency, AI can help companies save money. Predictive maintenance can also help companies avoid the cost of replacing equipment that has failed prematurely. In addition, by identifying potential problems before they occur, companies can schedule maintenance activities and replace components before they fail, saving money in the long run.

5. Better decision-making

AI algorithms can provide companies with valuable insights into their operations, enabling better decision-making. For example, by analysing data from equipment, companies can identify areas where they can improve efficiency or reduce costs.

Challenges and Limitations of Using AI in Predictive Maintenance

Despite the many benefits of using AI in predictive maintenance, there are also challenges and limitations that companies need to be aware of. These include:

1. Data quality

The quality of the data used to train AI algorithms is crucial. If the data is incomplete or inaccurate, the AI may not be able to predict equipment failures accurately. Therefore, companies must ensure that their data is high quality and covers a sufficient period for accurate predictions.

2. Cost

Implementing AI-based predictive maintenance systems can be costly, particularly for smaller companies. High upfront costs may be associated with acquiring the necessary hardware and software and ongoing maintenance costs.

3. Expertise

Developing and implementing AI-based predictive maintenance systems requires expertise in both AI and maintenance. As a result, companies may need to hire additional staff or outsource these services to experts to ensure the system is effective.

4. Integration

Integrating AI-based predictive maintenance systems with existing systems can be challenging. Companies may need to make significant changes to their existing infrastructure to ensure the system can be implemented effectively.

Conclusion

In conclusion, AI is becoming increasingly crucial in predictive maintenance for transport and logistics equipment. By analysing data from sensors and other sources, AI algorithms can predict equipment failures, recommend maintenance actions, and optimise maintenance schedules. The benefits of using AI in predictive maintenance include reduced downtime, improved safety, increased efficiency, cost savings, and better decision-making. However, there are also challenges and limitations associated with implementing these systems, including data quality, cost, expertise, and integration. Therefore, companies considering AI-based predictive maintenance systems must consider these factors carefully before proceeding.

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In this video, we are looking at how some familiar technologies are impacting or could impact the transport, distribution and logistics sectors. We will be covering the following:

• Artificial intelligence (AI)
• Blockchain
• Chatbots
• Cloud-based solutions and software-as-a-service models (SaaS)
• Internet of things (IoT)
• Mobile applications and location services
• Data science, analytics and big data.

Image Credit: Image by Drazen Zigic on Freepik

In this article, we will be looking at how some familiar technologies are impacting or could impact the transport, distribution and logistics sectors, covering:

• Artificial intelligence (AI)
• Blockchain
• Chatbots
• Cloud-based solutions and software-as-a-service models (SaaS)
• Internet of things (IoT)
• Mobile applications and location services
• Data science, analytics and big data.

Artificial intelligence (AI)

AI is a powerful tool that can assist with predictive maintenance and route optimisation. It can also help with predictive analytics, which gives you insight into how you can optimise your fleet’s performance.

Blockchain

What is blockchain?

Blockchain is a decentralised ledger that records transactions through a shared database. It’s been around since 2008 and has gained significant popularity because it’s transparent, secure and cost-effective. The data stored on the blockchain cannot be altered or hacked, making it a handy tool for companies in the logistics industry.

How does it work?

Blockchain uses cryptographic keys to record every transaction made on its network. Each block stores data such as transaction information and time stamps so that changes cannot be made without being tracked down quickly by other network users if need be. The blocks are linked together in chronological order forming what we know as “chains” because each block references those which come before or after it chronologically speaking (hence why they’re called chains). This technology allows for greater transparency within an organisation’s supply chain management (SCM) processes since everyone involved, e.g. with transportation, knows exactly where things stand at any moment instead of relying on guesswork as we’ve done before now!

Chatbots

Chatbots are computer programs that simulate human conversation. They can be used to answer questions and provide information, even on the fly. Many companies have started using chatbots as an alternative to phone calls or emails for customer service, including transport distribution and logistics companies. A chatbot could handle basic questions about delivery schedules and packages, answering those questions faster than a human would be able to do so.

Cloud-based solutions and software-as-a-service models (SaaS)

Cloud-based solutions and software-as-a-service models (SaaS) are becoming more common in the transportation industry. They offer a variety of benefits, including:

• Efficiency – Cloud computing can help businesses save money by reducing infrastructure costs and improving operational efficiency.
• Flexibility – Remote access means you don’t have to be on-site to get work done, which provides flexibility for staff members, suppliers, or customers who may be located in remote areas with poor access to traditional IT resources.
• Security – Cloud services are hosted offsite and managed by third parties, which means hackers will have a more challenging time gaining access to your company’s data compared with an onsite server that’s vulnerable if it gets hacked into by malware or other threats.
• Accessibility – Cloud services are available wherever there is an internet connection—and sometimes even when there isn’t one! This makes them ideal for companies operating in remote areas where they may not have easy access to traditional IT resources such as fibre optic cables needed for fast download speeds

Internet of things (IoT)

The Internet of Things (IoT) is the inter-networking of physical devices, vehicles, buildings and other items—embedded with electronics, software, sensors, actuators and network connectivity that enable these objects to collect and exchange data.

The term “internet of things” was coined by Kevin Ashton in 1999. Kevin Ashton first proposed the actual concept behind the internet of things in 1999 as part of a larger vision for the emerging ubiquity of RFID tags on products. He saw this as an opportunity to create a device allowing consumers to know precisely where their possessions were at any given time using GPS technology built into the tag. This idea has been further developed over time into different projects such as Radio Frequency Identification (RFID) tags used on customers’ clothing or accessories that let retailers track inventory levels at individual stores; barcodes printed on product packaging; wireless sensor networks that monitor environmental conditions like temperature or humidity; radio frequency identification tags embedded inside cars so they can be tracked remotely through toll booths at highway speeds, and even home appliances like refrigerators that connect wirelessly with smartphones via Bluetooth technology so people can receive notifications when food items reach their expiration dates!

Mobile applications and location services

Here are some examples of how mobile apps can transform the transportation and logistics industry:

• Scheduling, inventory, and tracking. Mobile apps enable you to manage the scheduling of your fleet in real-time. They also help improve fleet efficiency by reducing fuel consumption and maintenance costs through better driving practices. With an effective tracking system in place, you can ensure that your drivers are making deliveries on time, resulting in fewer customer delays and more satisfied customers.
• Customer service. Not only can you improve customer satisfaction by providing better services with an app, but it also gives customers a convenient way to interact with your business 24/7 via their smartphones or tablets. Access to information about orders placed on the go helps improve communication between customers and companies, which ultimately leads to better service quality across all channels (eCommerce sites included!).
• Marketing & sales activities such as promotions/discounts etc., could generate additional revenue streams for companies operating within this sector once they have been integrated into their existing business processes; enabling them to achieve higher ROI levels while unlocking new opportunities

Data science, analytics and big data

Data is the new oil. It is the currency of the digital age and is more valuable than gold. Data drives our digital economy, and it’s not just about social media posts or website visits anymore. Big data refers to large sets of structured data that can be analysed to discover patterns, trends and associations to predict future outcomes.

The world has changed so dramatically in recent years because we are now able to collect and analyse vast quantities of information quickly at a meagre cost – thanks mainly to advances in technology such as cloud computing, big data analytics platforms like Hadoop or Spark (which enable companies to process vast amounts of data very quickly), artificial intelligence (AI), machine learning algorithms etc.

Technology has never been more critical in the transport distribution and logistics industry.

Technology is changing everything, and the transport distribution and logistics industry is no exception. Technology has never been more critical than today to help companies succeed in a constantly evolving market.

The impact of new technologies on business is no longer limited to just a few industries and sectors; every company must adapt their operations to keep up with the growing pace of innovation or face extinction.

Businesses of all sizes need to be aware of how their competitors use digital technologies like AI (artificial intelligence), machine learning and automation—and they need an action plan to stay competitive by adopting those same technologies.

Conclusion

Many technologies can help with transport, distribution and logistics. Some of these are already being used, while others are still developing. In addition to technological advances, there are also social changes underway that will affect how we move goods around the world: climate change is increasing the number of extreme weather events like hurricanes; governments around the world are restricting carbon emissions, and our growing global population means there’s more pressure than ever on our resources (both human and natural). These factors have led organisations like UPS to develop new methods for reducing their environmental impact while still providing excellent service at competitive prices.

In conclusion, transportation distribution and logistics are essential components of a global economy. The rapid growth of e-commerce has put increased pressure on the industry, highlighting the need for automation to boost efficiency and productivity. However, this has not come without challenges at every step along the way – legal, ethical and financial issues have all impacted how far we’ve come and how fast we can go in transforming our industry into a truly digital one. Overall, there is much potential for technologies to change the industry for good completely.

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The Nigerian transport, distribution and logistics industry has grown very slowly for so many years. However, it is expected to experience a 4% compound annual growth rate (CAGR) growth. The slow growth that the industry has experienced in the past few years is mainly due to poor infrastructure and logistical issues. There are also issues relating to congestion on roads and delay in customs procedures.

As a result, in 2018, the country ranked 145 of 190 countries in the ease of doing business index and 112 in the 2018 Logistics Performance Index. The recent growth in the Nigerian logistics industry is sparked by significant infrastructural developments in Airways and Railways, growth in the e-commerce sector, developing export and manufacturing sector and improvement in ties with other countries.

Examples of the use of technology

There has been a steady rise in Nigeria’s logistics companies and ride-hailing apps. This rise is primarily related to the technologically induced growth that the e-commerce sector has experienced. It is now possible to track the location of goods on the move on mobile devices.

Different components of data warehousing, data mining, e-retailing and e-commerce, all essential operational elements for the logistics industry, are actively redefined by technology.

Benefits of technological development

Despite still being in its developmental stage in Nigeria, the logistics industry remains one of its fastest-growing industries. As of 2018, the Nigerian logistics sector was valued at 250 billion naira, which is a 50 billion naira rise from its value in 2017. The rapid growth can be attributed to technological innovations of the past decades and how they have opened the industry for growth.

Also, in the last decade, online shopping has become a part of Nigeria and has changed how Nigerians shop. Platform providers for online shopping have provided Nigerians with seamless and easy access to international and local items, including food, books, clothes, toiletries, furniture and electronics. Online shopping alone in 2019 generated $4.88 million in revenue with 20.5% CAGR expected growth for 2019-2023; this would result in a $10.2 million market volume by 2023.

The trends of online shopping in 2019 showed the fashion industry to be the leading arm with user penetration of 52.2% and $1,76 million in transaction volume. This is, however, expected by 2023 to reach 75.2%. Thanks to technology and ease of payments via electronic wallets, debit cards, mobile money and other modes of payment, it is expected that internet penetration is going to help the sector further grow and, in turn, increase the logistics industry.

Technological challenges

Due to the technological deficit facing the Nigerian logistics industry, a significant challenge faced is the inefficiency of customs and seaports services at the ports. The process of customs clearance is manual and slow. This lengthy process increases the time goods stay at ports to pass through inspection, thus increasing operational costs. This is a problem that would not have existed with cargo management systems.

Other significant challenges facing the logistics industry are a huge deficit in infrastructure, unstable electricity and a poor road network. These challenges have led to the inability of the logistics industry to reach its full potential.

Suggested solutions

Some of the upcoming technologies that can be introduced to address the technical deficit and close the gap existing in the Nigerian logistics and transportation industry include:

1. Control towers and GPS tracking.
2. Warehousing management systems.
3. Real-time fuel management systems.
4. Cargo management systems.
5. Information and communication systems like electronic data interchange (EDI).

How investors can get involved

Infrastructure is essential to the development of the logistics industry. The cost, reliability, cycle time and access are directly impacted by the level of integration and the health of the available infrastructure. To maintain a competitive logistics ecosystem, a strategic and constant upgrade of the infrastructure is needed. The financing for the much-needed infrastructure is where investors can get involved.

For example, the Nigeria Special Economic Zones Investment company is raising a capital of $250 million from different lenders to develop free trade zones in Lagos, Abia and Katsina states. Also, Nigeria is expected to build the Lekki Lagos Airport on a 3,000-hectare plot. Underway is the Lekki Deep Sea Port Project, with a budget of $1.5 billion. Investments in infrastructure are essential for the growth of the logistics sector.

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We are on a journey and this post is a humble beginning. We are super excited about the future, more so about what this site is all about – technology, innovation, inventions and all that good stuff!.

Science and technology revolutionize our lives, but memory, tradition and myth frame our response. – Arthur Schlesinger (Historian)

There are lots of untapped innovation waiting to be discovered and put to good use. Our vision at Akingate is to explore how existing or emerging technologies can result in tangible improvement to the quality of everyday lives.

We blog about tech development and creativity that’s making a difference in the lives of people or transforming industrial sectors. From the very simple innovations to complex inventions, count us in, we are interested.

Come along, enjoy the blog, learn and grow.

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