Trajectory planning for traffic safety with dynamic ethical risk adjustment

The integration of ethical principles into the trajectory planning of connected and automated vehicles remains a critical challenge, balancing technical efficacy with societal values. Current algorithms prioritize ego-vehicle safety but inadequately address ethical risks for all road users and cultural variations in moral preferences. This study proposes a trajectory planning algorithm with dynamically adjustable ethical risks, introducing two key innovations: (1) an "ethical knob" mechanism that flexibly weights risks between ego vehicles and other road user, enabling region-specific ethical customization, and (2) a hybrid subjective-objective weighting method combining Analytic Hierarchy Process and coefficient of variation to dynamically allocate weights among four ethical principles-utilitarianism, justice theory, deontology, and responsibility ethics. The algorithm embeds these frameworks into a safety potential field model, translating abstract ethics into computable risk costs. Multi-scenario simulations demonstrate that neutral ethical knob settings minimize overall risk costs, while dynamic weighting automatically adapts to environmental changes compared to static approaches. Crucially, the framework avoids predefined "trolley problem" dilemmas by focusing on accident prevention rather than post-collision decisions. By enabling cultural adaptability and transparent ethical trade-offs, this work advances interdisciplinary solutions for socially acceptable autonomous systems.