Artificial intelligence (AI) is rapidly emerging as a transformative force in reshaping transportation, offering significant advancements to improve safety, efficiency, and convenience for all road users. Key applications include autonomous vehicles and smart infrastructure systems, both employing advanced technologies to navigate and manage complex traffic environments. Autonomous vehicles, or self-driving cars, operate without human control by using AI algorithms to interpret sensor data, make real-time decisions, and safely maneuver through diverse and unpredictable traffic conditions. These vehicles utilize machine learning, computer vision, and deep learning to recognize objects, predict behaviors of other road users, and optimize routes. By minimizing human error—a major cause of accidents—self-driving cars have the potential to greatly enhance road safety and reduce traffic fatalities. Beyond individual vehicles, AI is revolutionizing traffic management via smart infrastructure. Adaptive signal control and smart traffic lights analyze live traffic data to adjust signal timings dynamically, mitigating congestion during peak hours and improving traffic flow. These systems can communicate with autonomous cars and other connected devices, forming integrated networks that enable smoother transit and reduce delays. Additionally, AI-driven transportation technologies contribute to environmental benefits by optimizing routes and decreasing stop-and-go traffic, thereby lowering fuel consumption and emissions—aiding cities in combating pollution and achieving sustainability goals. Despite these promising advances, several challenges must be overcome for widespread AI adoption in transportation.

Regulatory frameworks need updating to address the unique aspects of autonomous vehicles and smart infrastructure, ensuring public safety without hindering innovation. Policymakers must establish standards for vehicle testing, data privacy, and liability related to AI-driven systems. Ethical concerns also demand attention, especially regarding decision-making in emergency scenarios, accountability for machine errors, and impacts on employment within the sector. For example, dilemmas about how autonomous vehicles prioritize human lives in unavoidable accidents underscore the need for transparent, inclusive dialogues involving technologists, ethicists, lawmakers, and the public. Furthermore, integrating AI into transportation infrastructure requires significant investment in technology upgrades and cybersecurity to protect systems from potential cyberattacks, maintaining trust and preventing disruptive incidents. Collaborations among industry leaders, researchers, and governments are ongoing, with pilot programs and real-world testing expanding to generate data and insights for future advancements. In summary, AI is at the forefront of transforming transportation by enabling autonomous vehicles and smart infrastructure that promise safer, more efficient, and environmentally friendly mobility. However, fully realizing these benefits depends on addressing regulatory, ethical, and security challenges. Careful integration of AI into transportation systems will be crucial for shaping a future of mobility that is equitable, sustainable, and advantageous for all.