ZIMBABWEANS exhibit a strong appetite for motor vehicles, surpassing that of many other nations. Exploring the psychological factors driving this passion could be a fascinating topic for another day.

For now, let us delve into the connection between the global rising interest in driverless cars and artificial intelligence (AI).

The idea of driverless cars, once just a fantasy in science fiction, is quickly becoming a reality thanks to artificial intelligence (AI). The combination of AI and self-driving technology has the potential to change how we travel, improve road safety, and alter city designs.

As we enter this exciting new era, it is important to understand where the technology stands now, the challenges it faces, and what the future might hold.

Development and current status

The journey to create autonomous vehicles has been gradual, with many important milestones along the way. Early projects in the 2000s, like the Defense Advanced Research Projects Agency (DARPA) challenges, set the stage for today’s self-driving systems.

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In the 2010s, major companies like Google (now Waymo) and Tesla advanced the integration of AI, leading to the creation of advanced driver-assistance systems (ADAS) that help with driving and navigation.

Autonomous vehicles can be categorised as follows:

Level 1: Basic driver assist, like cruise control

Level 2: Advanced driver assist, like dynamic lane management or park assist

Level 3: Self-driving with human oversight at all times

Level 4: Self-driving without oversight under specific conditions

Level 5: Full self-driving under all conditions

As of 2024, the world of self-driving cars shows both progress and some cautious expectations. Fully autonomous cars (Levels 4 and 5) are not yet common, but there have been significant developments in Level 2 and Level 3 technologies, which offer partial and conditional automation.

Companies like Mercedes-Benz and BMW are at the forefront of adding Level 3 driver assistance features to their vehicles.

In the area of self-driving taxis, companies such as Waymo and Cruise are making good progress, although they still face challenges. Autonomous trucks are also becoming more popular, with increased testing and plans for expansion beyond traditional areas.

Key AI uses in driverless cars

AI is essential for the technology behind driverless cars, allowing them to see, think, and act in real-time. The main uses of AI in autonomous vehicles include:

Perception and data processing: AI algorithms, especially those using deep learning, process information from various sensors (like cameras and radar) to understand the car's surroundings, detect objects, identify lanes, and recognise traffic signs.

Decision making and control: Advanced AI models, including reinforcement learning and neural networks, make real-time decisions based on the processed information, helping the car navigate traffic, follow signals, and avoid obstacles.

Simulation and training: Generative AI and self-supervised learning allow self-driving cars to be trained in virtual settings, simulating many driving situations and reducing the need for extensive real-world testing.

Continuous learning and adaptation: Machine learning enables autonomous vehicles to keep learning and adapting to new environments and situations, improving their performance over time.

Safety and efficiency: AI helps improve various driving aspects, such as optimising routes, managing traffic, and driving in an energy-efficient way.

Technological challenges, progress

Despite the advancements, there are still several technological challenges to overcome before self-driving cars can become mainstream.

Achieving Levels 4 and 5 autonomy requires more advanced AI and machine learning techniques that can handle complex real-world situations.

Current AI technology, while impressive, is not yet reliable enough to ensure the necessary safety.

Improvements in AI, like more advanced neural networks and better machine learning methods, are constantly pushing the limits of what is possible.

Companies like Oxa are using generative AI to train autonomous vehicle software in virtual environments, allowing for extensive testing without the need for real-world driving.

Regulatory landscape

The introduction of autonomous vehicles raises many regulatory and ethical questions. Different countries have various rules regarding self-driving cars, making international development and deployment complicated.

In the United Kingdom, the recent Automated Vehicles Act aims to put the country at the forefront of self-driving technology, with plans to have autonomous cars on the roads by 2026.

Ethical issues, such as who is responsible in accidents involving self-driving cars and how AI makes quick decisions in critical situations, are still being debated.

Developing explainable AI models is vital for building trust and transparency, which are essential for public acceptance and regulatory approval.

Societal impact

The widespread use of autonomous vehicles could greatly impact society, from reducing traffic accidents to changing urban planning. However, public acceptance is a key factor.

Despite the potential benefits, many people are hesitant to fully trust self-driving technology. Concerns about job losses in the transport sector and the shift from human drivers to machines contribute to this reluctance.

Industry developments

Several companies are leading the way in developing autonomous vehicles:

Waymo and Cruise: These companies have launched self-driving taxis that rely heavily on AI for navigation and decision-making, trained to handle various driving scenarios.

Tesla: Known for its Autopilot and Full Self-Driving features, Tesla continues to push the limits of consumer-accessible autonomous technology.

Traditional car makers: Companies like Mercedes-Benz, BMW, and Ford are investing significantly in autonomous technology, adding advanced ADAS features to their vehicles.

Future outlook

The future of self-driving cars looks promising but uncertain. By 2035, the autonomous driving market could generate between £300 billion (US$387,8 billion) and £400 billion (US$517,1 billion) in revenue, driven by advancements in AI and new business models like pay-as-you-go and subscription services.

In the short term, we can expect continued improvements in Level 2 and Level 3 technologies, with more vehicles incorporating advanced driver-assistance features.

The gradual rollout of self-driving taxis and trucks will provide valuable data and insights, helping to refine and enhance autonomous systems.

Conclusion

The combination of AI and driverless cars represents a major shift in the automotive industry, with the potential to greatly improve road safety, reduce traffic congestion, and create more sustainable urban environments.

While the path to full autonomy is complex and challenging, the steady progress being made suggests a future where self-driving vehicles become a regular part of our lives. As technology continues to evolve, cooperation between manufacturers, regulators, and the public will be crucial in tackling the various challenges ahead, paving the way for a safer, more efficient, and innovative transport system.

  • Bangure is a filmmaker. He has extensive experience in both print and electronic media production and management. He is a past chairperson of the National   Employment Council of the Printing, Packaging and Newspaper Industry. He has considerable exposure to IT networks and Cloud technologies and is an enthusiastic scholar of artificial intelligence. — naison.bangure@hubedutech.com.