Autonomous vehicles (AVs) offer a promising glimpse into a future where transportation is smarter, safer, and more streamlined. Nevertheless, as AVs continue to interact with conventional vehicles (CVs), the potential for increased complexities and challenges cannot be overlooked, such as the frozen robot problem. This study proposes a regret-based model for motion planning responsibilities, encompassing self-respect and courtesy for conflicting personal interests. By incorporating these reciprocal responsibilities, socially compatible driving behaviors are promoted, and uncertainties in behavior are also reduced. A Self-Respect-Courtesy (SR-C) plane is further introduced, illustrating the interaction intensity and tendency. To navigate the trade-offs of responsibilities in varying situations, the concept of environmental niche is provided. Niches help to characterize the outcomes of specific actions with the resulting conditions to fulfill responsibilities. Finally, a hierarchical multi-agent inverse reinforcement learning algorithm is designed to calibrate the proposed model with NGSIM highway lane-changing cases. We found that the proposed model can significantly improve the calibration results and reduce the predictions error of mandatory lane changes by up to 20%. Moreover, the cross-entropy error also significantly decreases in a stable stage, indicating that responsible actions can safely reduce the behavior uncertainties of interactions. Our research revealed that drivers prioritize courtesy responsibility in discretionary lane changes with more consistency, whereas their self-respect preferences are stronger but show more variability in mandatory lane changes. These findings provide valuable insights into the underlying mechanism of interactions.

The emerged integrated chassis is playing an increasingly pivotal role in enhancing vehicle stability, which typically needs estimation of the stability envelope and proper coordination strategy for chassis controller design. The conventional stability envelope determined in phase plane ensure vehicle self-stability, while it neglects the influence of chassis actuators. To play out full performance of integrated chassis, this study proposes a novel extended stability envelope analysis method to quantify the effect of different chassis actuator combinations on vehicle lateral dynamic. In energy phase plane, the state change direction with influence of control inputs are employed to judge whether a given state will go beyond the tire’s grip limit. Accordingly extended stability envelopes of some typical actuator combinations, i.e., active front steering (AFS) and active rear steering (ARS), AFS and torque vectoring control (TVC), ARS and TVC, are determined. Meanwhile, principle to quantify the overlapping effectiveness range and unique effectiveness range between ARS and TVC is introduced, accordingly effectiveness factors used for coordination control are discussed. In summary, the proposed method in this article has considerable potential for coordination control of integrated chassis, by providing comprehensive analysis method to estimate the extended stability boundary and quantify the effectiveness range with influence of actuators taken into account.