V2X is used for communication between the surrounding pedestrians, vehicles, and roadside units. In the Forward Collision Warning (FCW) of Phase One scenarios in V2X, multimodal modalities and multiple warning stages are the two main warning strategies of FCW. In this study, three warning modalities were introduced, namely auditory warning, visual warning, and haptic warning. Moreover, a multimodal warning and a novel multi-staged HUD warning were established. Then, the above warning strategies were evaluated in objective utility, driving performance, visual workload, and subjective evaluation. As for the driving simulator of the experiment, SCANeR was adopted to develop the driving scenario and an open-cab simulator was built based on Fanatec hardware. Kinematic parameters, location-related data and eye-tracking data were then collected. The results of the Analysis of Variance (ANOVA) indicate that the multimodal warning is significantly better than that of every single modality in utility and longitudinal car-following performance, and there is no significant difference in visual workload between multimodal warning and the baseline. The utility and longitudinal driving performance of multi-staged warning are also better than those of single-stage warning. Finally, the results provide a reference for the warning strategy design of the FCW in Intelligent Connected Vehicles.

Conditionally automated driving (CAD) systems allow the driver to temporarily disengage from driving tasks. The significant concern of CAD is to ensure a safe and timely transition to the manual driving mode when the system exceeds its limits and issues a takeover request (TOR). The aim of this study was to investigate the effect of directional auditory TOR on the driver takeover process. A within-subject experimental design was used: twenty-four participants drove four times in an automated driving simulation scenario and received two non-directional auditory TORs and two directional auditory TORs (TOR directions corresponding to the orientation of potential hazards). The takeover behavior and eye movement characteristics relevant to the takeover process were collected. The results showed that directional auditory TOR enabled drivers to shift their visual attention to the hazards’ area faster than non-directional auditory TOR, accelerating the driver’s understanding of the road environment and improving takeover behavior. The study may provide a reference for the design of takeover requests for conditionally automated driving.

The existing forward collision warning (FCW) systems that adopt kinematic or perceptual parameters have some drawbacks in the warning performance because of poor adaptability to the users or ineffectiveness of the warnings. To solve the problems of adaptability, several FCW models have been proposed based on algorithms (machine learning, deep learning). However, there is a lack of consideration for the multi-staged warning to avoid an abrupt warning that may startle or distract the driver. In this study, a light gradient boosting machine (LGBM) was adopted to develop a multi-staged FCW. The proposed model was trained and evaluated on a platform based on a driving simulator by twenty drivers. Through Shapley Additive Explanations (SHAPs), the output of the proposed model was explained. Specifically, the front vehicle acceleration, time-to-collision (TTC), and relative speed were found to strongly affect the warning stages from the proposed model. To evaluate the utility and acceptability of the developed model, it was compared with three existing FCW models in terms of subjective and objective indicators. As a result, a trade-off was found between the utility and user acceptance. Additionally, the comparison study also indicated that the developed model outperformed other previous models due to not only the high accuracy but also the suitable trigger timing for each participant.

In this study, the usability evaluation model is constructed for a co-pilot screen, and an analysis of the impact factors and optimization recommendations is made based on the evaluation results. Firstly, based on the usability design principles, interaction ease, interaction efficiency, visual comfort, driving safety, and their corresponding secondary indicators are defined, and the subjective weight of each indicator is determined using the analytic hierarchy process (AHP). Then, usability evaluation is carried out on four vehicles via vehicle driving simulated experiments and driving experiments on the road, and the objective weight of the indicators is determined using the CRITIC method. Finally, the usability evaluation model for co-pilot screens is established by applying the fuzzy comprehensive evaluation method. The results indicate that the overall usability comprehensive score of co-pilot screens is convergent and is mainly concentrated in the range of 50–65 points, with two vehicles having excellent affiliation and two vehicles having average affiliation. However, there is a great distance still to reach when compared to an excellent level. The usability evaluation model of co-pilot screens established in this article can quantify the HMI usability design of co-pilot screens. The results of this study are significant for the four tested vehicles in terms of guiding the usability design of co-pilot screens and in promoting the rapid iteration of co-pilot screen development. And a production vehicle that connects a driving simulation platform and the usability evaluation model can be used to test and evaluate more screen designs, interaction models, tasks, and infotainment applications, thus guiding further user experience designs.