AI-Enabled Transportation: A Safer and Smarter Future

Author : Emma Claridge, Display Technology

12 October 2023

Figure 1: Automobile HMIs are transitioning from just being incorporated into central infotainment columns - with displays being installed throughout the cabin plus head-up display units becoming increasingly popular
Figure 1: Automobile HMIs are transitioning from just being incorporated into central infotainment columns - with displays being installed throughout the cabin plus head-up display units becoming increasingly popular

Smart transportation is promising a future that is characterised by efficiency and innovation. At the heart of this transformation lie 3 key technologies. These are artificial intelligence (AI), machine learning (ML) and embedded systems.

Together, they are set to reshape the way we travel - making it safer and more convenient, as well as having greater responsiveness to our needs. This article looks at the human-machine interface (HMI) and information delivery aspects. 

In the not-so-distant past, self-driving cars were confined to the realm of science fiction. Today, AI-driven autonomous vehicles are becoming a more serious prospect. Powered by sophisticated AI algorithms and supported by data coming from an array of different sensors, such vehicles are fully aware of their surroundings with precision way beyond human capabilities, ensuring a higher level of safety on the roads. They can navigate their way around complex urban environments and react quickly to any sudden situational changes that might occur, so that accidents are prevented. 

An important part of the embedded hardware supporting AI/ML workloads will be the thin-film transistor (TFT) displays. It is through these the data needed by vehicle drivers, commuters, traffic management personnel and municipal staff will be accessed. 

Defining the roads of tomorrow
Perhaps one of the most compelling advantages of AI-driven autonomous vehicles is their adaptability to ever-changing traffic conditions. Traffic congestion is a perpetual challenge in modern cities, causing not only delays but driver frustration too. By leveraging AI, autonomous vehicles can analyse vast amounts of data, from live traffic updates to historical patterns, enabling them to dynamically alter their routes in accordance with the traffic conditions at that time. As a result, travel time is optimised and congestion issues lessened. 

AI is central to ensuring safety in autonomous vehicles, as the ML technology incorporated into them is continuously learning from driving scenarios, minimising the chance of potential human errors. AI-enabled vehicles create an interconnected fleet through vehicle-to-vehicle (V2V) communication, sharing real-time data on road conditions and any hazards that have been identified. 

Greater driver assistance
Advanced driver assistance system (ADAS) technology is gaining ever greater traction within contemporary vehicle models - and provides the first indications or where automotive AI is going to eventually take us. Features, like lane-keeping and collision avoidance, leverage real-time data to support drivers and reduce the errors made. ADAS acts as a vigilant co-pilot. Instead of replacing human drivers, in the current semi-autonomous cars being introduced, AI complements their abilities - acting as an overseer and taking the wheel during challenging circumstances.

Embracing the future: Predictive maintenance via ML
In transportation, breakdowns and repair/maintenance issues have been a constant nuisance. But now, a new era of enhanced operation is starting to emerge, via the transformative concept of predictive maintenance. Sensors can meticulously monitor various aspects, such as engine performance, temperature, oil quality, tyre pressure and numerous other crucial parameters. By analysing acquired sensor data, taken from throughout the vehicle, it is possible to identify patterns, anomalies and potential signs of wear and tear. AI algorithms can then accurately estimate when vehicle servicing or component replacement will be required.

Predictive maintenance is a valuable tool in preventing catastrophic failures too. ML-powered insights detect early signs of degradation, enabling timely alerts to maintenance crews. This proactive approach eliminates unplanned downtime, improving fleet productivity and customer satisfaction. Furthermore, it extends vehicle lifespan and reduces repair costs.

Improving urban mobility
Urbanisation and population growth have led to overcrowded cities worldwide. However, AI-driven traffic management offers an effective solution by enabling smoother flow and shortened travel times. These can process real-time data from cameras and sensors, then make informed decisions to optimise signal timings and dynamically reroute traffic.

Traditional static traffic signal timings, based on historical patterns, often struggle to handle urban traffic's inherent unpredictability. With AI in charge, traffic control systems become agile and adaptive. AI-driven models continuously monitor traffic movements, considering vehicle density, speed, flow patterns, etc. This real-time analysis enables traffic signals to adjust timings on-the-fly - better aligning with ever-changing road dynamics.

The impact of this more sophisticated traffic management is profound, benefitting the urban landscape in various ways. Optimised signal timings lead to more efficient intersections and a smoother movement of vehicles. Reduced stop-and-go patterns minimise congestion, saving drivers from idle time, resulting in significant fuel savings and lower emissions.

Figure 2: HMI technology will have a key role to play in the transition to higher levels of vehicle autonomy
Figure 2: HMI technology will have a key role to play in the transition to higher levels of vehicle autonomy

AI's predictive capabilities mean that traffic management systems can anticipate congestion hotspots and take measures to alleviate them. By redirecting traffic away from the most heavily congested areas, AI redistributes loads, easing bottlenecks and dispersing traffic more evenly across the road network. This approach reduces delays and enhances safety by mitigating the risk of accidents caused by overcrowding. Furthermore, AI integration enables cities to respond swiftly to unforeseen events and emergencies. Whether rerouting traffic for public events or diverting vehicles from accident scenes, AI adapts rapidly, ensuring efficient traffic management in any situation.

Intelligent public transportation
By analysing data on demand and traffic flow, intelligent public transportation optimises routes, schedules and service frequency. Commuters can enjoy timely transit options, reducing wait times and streamlining travel. The benefits extend to entire communities, with reduced traffic congestion and improved air quality, fostering a greener and more sustainable urban environment.

AI in public transportation offers useful insights to city planners too. Continuous learning and refining of AI algorithms enable precise planning and resource allocation, optimising investments in transit infrastructure. Beyond route optimisation, AI enhances safety with video analytics detecting anything out of the ordinary and responding swiftly to potential threats. The seamless integration of AI means personalised experiences can be derived through real-time updates and customised itineraries, encouraging more individuals to make use of public transit.

Real-time passenger information
Real-time information updates, leveraging TFT displays in vehicles and transport hubs, can majorly benefit passengers. They can receive up-to-the-minute details in relation to schedules and routes, plus news about any delays (because of signal failures, late running services and suchlike). Additionally, this technology fosters inclusivity, benefiting passengers with special needs. 

Better infrastructure management
Smart infrastructure management, with its integration of embedded systems and AI, offers a means to monitor and manage essential items of transportation infrastructure, such as bridges and roads. By continuously analysing data on structural integrity, traffic flow and environmental conditions, the system can detect minute cracks in a bridge's concrete or determine the gradual shifting of structural components.

With early detection of these warning signs, smart infrastructure management can prompt maintenance crews to take corrective measures promptly, preventing minor concerns from escalating into major disasters. This not only ensures the safety of commuters, but also extends the lifespan of infrastructure, making it more resilient and sustainable.

Furthermore, the potential of smart infrastructure management extends to traffic management, with AI suggesting dynamic traffic rerouting strategies based on real-time data. This minimises the impact of road closures or construction work and helps to uphold good commuter experiences.

Optimised fleet management
Optimised fleet management uses ML algorithms to scrutinise data on routes, fuel efficiency, vehicle allocation, etc. Through it, companies achieve substantial cost reductions and enhanced productivity. ML-driven data analysis provides comprehensive insights into fleet operations, optimising route planning, fuel usage, vehicle allocation, etc. The benefits of this technology extend further than simple cost reduction. By boosting operational efficiencies, companies can increase productivity, carry out a greater number of deliveries and serve more customers. This optimised approach enables businesses to scale their operations without compromising on performance. The result is minimised fuel costs, on-time deliveries and reduced environmental impact.

A new age of smarter mobility
Smart traffic safety relies on AI to identify prospective hazards. AI-driven video analytics monitor traffic, detecting violations like reckless driving and speeding. Subsequent action by law enforcement officers ensures compliance with traffic laws and makes roads safer. Beyond violations, AI systems analyse extensive traffic data to identify accident-prone areas. Predicting high-risk locations allows authorities to implement preventive measures, such as improved signage or road adjustments. With AI as an extra watchful eye on the road, lives can be saved by quickly detecting dangers and addressing unsafe behaviours - reinforcing ongoing efforts to prioritise the wellbeing of all road users.

Through the high-resolution display products, robust touchscreens precision touch controllers and embedded hardware that Display Technology supplies, the company is able to assist customers in implementing the systems needed for complex AI/ML workloads. It therefore has a pivotal role to play in the progression of smart transportation here within the UK.  

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