Hidden toll of AI-driven transport: Anxiety, isolation, depression

The study highlights that road safety remains a pressing concern, with over 42,000 fatalities in the United States in 2022 alone. According to the report, 90% of these accidents are attributed to human error. AI, especially deep learning, is addressing this by enhancing detection, prediction, and decision-making through advanced driver assistance systems (ADAS) and the emergence of Connected and Autonomous Vehicles (CAVs).

As global cities grapple with worsening traffic congestion, air pollution, and road fatalities, artificial intelligence is emerging as a cornerstone technology to reimagine transportation systems - yet its deployment may inadvertently exacerbate mental health issues like depression, especially in overburdened urban environments.

A newly published comprehensive study titled "The Role of AI in Smart Mobility: A Comprehensive Survey", authored by Marco Del-Coco and colleagues and released in Electronics (2025), delves deep into the state-of-the-art applications of AI in smart transportation. The survey addresses three primary pillars of AI in mobility: smart vehicles, smart urban planning, and vehicular network security. While the innovations promise to revolutionize safety and efficiency, the authors also indirectly underscore a complex reality - an increasingly connected, data-driven transportation infrastructure may have unintended consequences for public mental health, notably rising levels of depression triggered by algorithmic control, surveillance, and technological overload.

How are AI-enabled smart vehicles shaping road safety and human interaction?

The study highlights that road safety remains a pressing concern, with over 42,000 fatalities in the United States in 2022 alone. According to the report, 90% of these accidents are attributed to human error. AI, especially deep learning, is addressing this by enhancing detection, prediction, and decision-making through advanced driver assistance systems (ADAS) and the emergence of Connected and Autonomous Vehicles (CAVs).

These systems rely heavily on a fusion of sensors, LiDAR, radar, cameras. and deep learning models for real-time object recognition, behavior prediction, and emergency intervention. AI systems can now predict pedestrian trajectories, analyze body language, and estimate risks in low-light and occluded scenarios using algorithms like YOLO, SSD, and foundation models such as SAM. Yet, the replacement of human judgment with machine prediction introduces new psychological stressors. Constant monitoring and reliance on machines may provoke a sense of loss of control among drivers and pedestrians alike - an emerging factor in urban depression trends.

Moreover, smart road infrastructure integrated with semantic scene understanding, 3D mapping, and adaptive recognition of traffic signs and lane markings forms an ecosystem that leaves little room for spontaneity. While this automation reduces crashes, it potentially contributes to what psychologists term “technostress” - a form of anxiety and depression driven by overexposure to automated decision-making systems.

Can AI solve urban traffic and pollution without deepening social fragmentation?

The study's second pillar explores smart urban planning, where AI models are being used to predict traffic patterns, optimize public transport, and forecast air quality. Spatiotemporal neural networks, graph-based learning models, and hybrid AI architectures are employed to forecast vehicle flows, travel times, and pollution levels across cities like Beijing, Madrid, and New York. These systems power dynamic rerouting, pollution warnings, and real-time navigation for millions of commuters.

However, the increased dependence on predictive mobility tools, ride-sharing algorithms, and AI-driven congestion management raises significant ethical and psychological questions. Algorithmic choices about routing, prioritization, and speed regulation can lead to perceived biases and exclusion, exacerbating feelings of powerlessness, especially in marginalized communities. Citizens who feel dictated to by black-box systems may experience rising distrust in public infrastructure, reinforcing depressive tendencies tied to institutional alienation.

The deployment of AI in pollution monitoring, though revolutionary, adds another layer of psychological exposure. While real-time AQI alerts help mitigate physical health risks, they can trigger environmental anxiety, especially among youth and vulnerable populations already grappling with climate-related depression.

Are smart vehicle networks and AI-based surveillance feeding into public depression?

AI's integration into vehicle network security marks a critical advancement but also introduces profound implications for mental well-being. The study details how Vehicle-to-Everything (V2X) communication systems, ranging from Vehicle-to-Vehicle (V2V) to Vehicle-to-Infrastructure (V2I), rely on machine learning for anomaly detection, privacy protection, and attack prevention.

Intrusion detection systems powered by LSTM, CNN, and federated learning monitor vast streams of driver and vehicle data to thwart cyberattacks, such as spoofing and jamming. However, this level of data harvesting, often without transparent user consent, can lead to “data fatigue” and “surveillance stress,” both of which are identified psychological risk factors for depression.

Further complicating the mental health landscape, the use of adversarial machine learning attacks and defensive mechanisms creates an atmosphere of digital paranoia. The need for continual behavioral adaptation in such high-surveillance environments, where even pedestrian gestures are algorithmically interpreted, can foster chronic anxiety, loss of agency, and social withdrawal.

This digital hyper-vigilance is particularly problematic in urban spaces where AI-powered infrastructure continuously tracks movement, intentions, and even mood through facial expression recognition. As the line between public infrastructure and private emotion becomes increasingly blurred, societal resilience may be tested by an emerging epidemic of context-driven depression.

The path forward: Techno-optimism vs. psycho-social reality

While the study concludes with optimism about AI’s potential to create safer, greener, and more efficient cities, it also calls attention to challenges including data scarcity, computational limitations, ethical ambiguities, and privacy risks. These issues, if left unaddressed, could inadvertently escalate emotional exhaustion, digital alienation, and depression, particularly among urban dwellers immersed in AI-mediated transportation ecosystems.

As policymakers and technologists push forward with the smart mobility agenda, the integration of psychological safeguards and human-centered design becomes imperative. Solutions could include transparent algorithmic governance, opt-out mechanisms, and urban AI systems co-designed with public health experts.