Road transportation plays a central role in modern societies, enabling economic activity, social inclusion, and mobility. Despite major technological advances, road accidents remain a critical societal challenge, with driver inattention, fatigue, and emotional states among the leading contributing factors. In Europe, road safety is a strategic priority, reflected in long-term policy ambitions such as reducing fatalities and serious injuries towards zero and strengthening vehicle safety through intelligent, human-centred technologies.
In this context, advanced driver monitoring systems have emerged as a key technological response. However, existing solutions largely focus on isolated indicators—such as eye closure, phone use, or head pose, often leading to fragmented interpretations of driver state and a high rate of false or poorly timed alerts. These limitations reduce system effectiveness and can undermine driver trust. There is therefore a clear need for more holistic, cognitively informed approaches that go beyond technical detection and address how drivers perceive, process, and respond to safety interventions.
The EmotAI project was conceived to address this gap by developing a new generation of driver state monitoring grounded in artificial intelligence, affective computing, and cognitive psychology. The overall objective of the project was to achieve a holistic understanding of the driver’s emotional and attentional state by integrating multiple complementary pillars: attention, activation (emotional and physiological state), driving performance, and external contextual factors. By combining these dimensions, EmotAI aimed to move from fragmented detection towards coherent interpretation of driver state over time.
A central ambition of the project was not only to improve detection accuracy but also to enhance the effectiveness of safety interventions. To this end, EmotAI introduced a cognitive control perspective, focusing on how alerts can be designed to meaningfully influence driver attention and emotional state rather than merely signalling risk. This approach reflects a clear pathway to impact: improved understanding of driver cognition enables better-designed alerts, which in turn can support safer driving behaviour, reduce accident risk, and increase acceptance of intelligent safety systems.
The project is embedded in a broader political and strategic context that promotes trustworthy, human-centric artificial intelligence and safer, more sustainable mobility. By aligning technological innovation with societal needs and regulatory trends, EmotAI contributes to ongoing efforts to strengthen road safety, support the deployment of intelligent transport systems, and enhance Europe’s leadership in responsible AI.
Social sciences and humanities played a key role in the project, particularly through the integration of cognitive psychology and human factors research. Psychological theories of attention, emotion, and cognitive load informed both the interpretation of driver state indicators and the design of cognitively appropriate alerts. This interdisciplinary integration ensured that the project addressed not only what can be detected by AI systems, but also how drivers cognitively and emotionally respond in real driving contexts. As a result, EmotAI positions itself as a human-centred innovation with the potential for significant scientific, societal, and industrial impact at scale.
