Chain-Like Cognitive Processes in Human-Machine Co-Driving: Uncovering Causal-Chain Mechanisms of Driver Takeover Failure

Takeover failure in conditionally automated driving is rarely caused by a single erroneous action; instead, it emerges through a causal chain in which risk is transmitted and accumulated across successive takeover process. Uncovering this mechanism is therefore essential for safe human-machine interaction. However, modeling the takeover process remains challenging because it involves multimodal and time-varying factors, lacks an explicit process-level mediator, and requires causal attribution under chain-like propagation. To address these challenges, this study proposes DBSE-SOR C2Net, an interpretable and inferable causal-chain network grounded in the cognitive framework of stimulus(S)-organism(O)-response(R). Specifically, the takeover reaction process (O) is introduced as the key mediator linking the pre-takeover states (S) to the post-takeover performance outcomes (R), and is further decomposed into nodes of the postural, perceptual and cognitive stage (PPC). To capture multimodal temporal characteristics, a multi-timescale encoding scheme is developed to jointly model long-horizon baselines and short-horizon perturbations in physiological and environmental signals. On this basis, a hierarchical dynamic Bayesian structural equation solution is constructed, with a bottom measurement layer for latent construct mapping and a top mechanism layer for dependency learning. Based on a human-in-the-loop takeover experiment (n = 41), the proposed framework effectively uncovers representative high-risk failure chains, including time-pressure-driven, cognitive-bottleneck, and multi-stage prolongation patterns. Under this framework , the adaptive takeover requests enhancement reduced PPC-stage reaction times by 0.11 s, 0.07 s, and 0.45 s, respectively, and shortened the overall takeover time by 0.31 s. Moreover, downstream PPC-stage monitoring with Minimum Risk Maneuver intervention reduced the collision rate by 75%. In general, the DBSE-SOR C2Net provides a backward reasoning and forward propagation paradigm for failure-chain diagnosis and adaptive safety intervention in automated driving.