A perspective way for judging tunnel approach zones by cognitive-behavioral chains and predictive processing model

In tunnel approach zones (TAZs), drivers must complete a sequence of tasks, including detecting the tunnel, identifying speed limits, and decelerating to enter safely. However, current standards mandate only stopping sight distance (SSD) compliance of TAZs, which may not suffice for all of these complex driving tasks. In this study, we investigated (1) whether SSDs are sufficient for driving tasks in TAZs, (2) the impacts of restricted visibility conditions on cognitive-behavioral processes, and (3) the appropriate visibility condition of TAZs. We selected tunnels with three visibility conditions to conduct both subjective tests of perception and experiments with real vehicles. We propose a research framework called the task analysis of driving scenarios modified predictive processing model (TADS-MPPM). We then construct a multidimensional framework that includes sequences of behaviors and cognitive tasks (with 4 driving behavior nodes and 4 cognitive nodes) for spatiotemporal profiling, as well as active deceleration coefficients (safety and efficacy coefficients) and cognitive-behavioral workload (measured using the extended Jaccard coefficient). Then, we use an MPPM to visualize the evolution of driving predictions, driving behaviors, and sensory inputs during the approach to the tunnel. Finally, we explore the risk mechanisms of TAZs. The results show that SSD designs (1) delay tunnel detection, speed-limit recognition, and deceleration initiation, as well as compressing behavioral-cognitive chains, and (2) degrade safety and compliance due to overloaded operations and cognition. Conversely, ensuring that critical tunnel information is discernible at a longer decision sight distance provides the necessary margin of safety on the road. This creates adequate space and time to perform progressive deceleration to eliminate task compression and restore composed and smooth driving maneuvers.