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Studying the rear-end early warning methods of connected automated vehicles (CAVs) is useful for issuing early warnings and reducing traffic accidents. Establishing a corresponding driving model according to CAV characteristics is necessary when designing intelligent decision and control systems, especially for the safety speed threshold. However, since traffic systems are stochastic, there are random factors that influence car-following behavior. Therefore, this study proposes a rear-end collision warning method for CAVs based on a stochastic local multivehicle optimal speed (SLMOV) car-following model. First, the SLMOV model is proposed to characterize the car-following behavior of CAVs. Simultaneously, a stability analysis and parameter estimation method are discussed. Second, the safety distance between the CAVs changes with time because the speed of the rear vehicles satisfies the SLMOV model, which is used to calculate the safety probability of rear-end CAV collisions through an analysis of the driving process. The speed threshold is assessed by controlling the rear-end collision probability. Third, next-generation simulation (NGSIM) data are used in an empirical analysis of a rear-end collision warning method on the basis of a parameter estimation of the SLMOV model. The results present the speed thresholds of vehicles under different braking deceleration levels. Finally, the merits and demerits of fixed-speed and variable-speed adjustment time intervals are compared by considering driving safety and comfort as evaluation indexes. A reasonable CAV adjustment time interval of 0.4 s is determined. This result can be used to help develop a vehicle loading rear-end collision warning system. [ABSTRACT FROM AUTHOR]

• Three ICV environment HMI groups: control, warning, and guidance, were designed to study their impact on driver's performance. • Warning group exhibited cautious and smooth acceleration before merging, while the Guidance group demonstrated better speed control after merging. • Mainline vehicle gaps had no significant impact on merging positions across HMI groups. • Guidance group merged closest to taper, Control group farthest. • Abundant gaps led to smaller initial merging gaps and shorter adjustment times for Guidance group. Intelligent Connected Vehicle (ICV) is considered one of the most promising active safety technologies to address current transportation challenges. Human-Machine Interface (HMI) plays a vital role in enhancing user driving experience with ICV technology. However, in an ICV environment, drivers may exhibit excessive reliance on HMI, resulting in diminished proactive observation and analysis of the road environment, and subsequently leading to a potential decrease in drivers' situational awareness. This reduced situational awareness may consequently lead to a decline in their overall engagement in driving tasks. Therefore, to comprehensively investigate the impact of HMI on driver performance in various ICV environments, this study incorporates three distinct HMI systems: Control group, Warning group, and Guidance group. The Control group provides basic information, the Warning group adds front vehicle icon and real-time headway information, while the Guidance group further includes speed and voice guidance features. Additionally, the study considers three types of mainline vehicle gaps, namely, 30 m, 20 m, and 15 m. Through our self-developed ICV testing platform, we conducted driving simulation experiments on 43 participants in a freeway interchange merging area. The findings reveal that, drivers in the Guidance group exhibited explicit acceleration while driving on the ramp. Drivers in the Guidance and Warning groups demonstrated smoother speed change trends and lower mean longitudinal acceleration upon entering the acceleration lane compared to the Control group, indicating a preference for more cautious driving strategies. During the pre-merging section, drivers in the Warning group demonstrated a more cautious and smooth longitudinal acceleration. The Guidance group's HMI system assisted drivers in better speed control during the post-merging section. Differences in mainline vehicle gaps did not significantly impact the merging positions of participants across the three HMI groups. Drivers in the Guidance group merged closest to the left side of the taper section, while the Control group merged farthest. The research findings offer valuable insights for developing dynamic human–machine interfaces tailored to specific driving scenarios in the environment of ICVs. Future research should investigate the effects of various HMIs on driver safety, workload, energy efficiency, and overall driving experience. Conducting real-world tests will further validate the findings obtained from driving simulators. [ABSTRACT FROM AUTHOR]

Highlights • The FOT data from 46 participants was used to analyze the effectiveness of drivers' characteristics, including gender, occupation and experience, on driving behaviors on a freeway deceleration lane. • Male drivers recognize risk more than female drivers, and also have more aggressive driving tendencies. • Professional drivers and experienced drivers made the last lane-change as early as possible to enter the deceleration lane. • The speed of the vehicles entering the exit ramp was significantly higher than the design speed. • The minimum TTC and the maximum deceleration show that the certain driving behaviors are related to high traffic risk. Abstract Deceleration lanes improve traffic flow by reducing interference, increasing capacity and enhancing safety. However, accident rates are higher on these interchange segments than on other freeway segments. It is important to attempt to reduce traffic accidents on these interchange segments by further exploring the behavior of different types of drivers on a highway deceleration lane. In this study, with field operational test (FOT) data from 89 driving instances (derived from 46 participants driving the test road twice) on a typical freeway deceleration lane, section speed profiles, vehicle trajectories, lane position and other key parameters were obtained. The lane-change characteristics and speed profiles of drivers with different genders, occupations and experiences were analyzed. The significant disparities between them reflects the risk associated with different groups of drivers. The study shows that male drivers changed to the outside lane earlier; professional drivers and experienced drivers made the last lane change as early as possible to enter the deceleration lane; and the speed of the vehicles entering the exit ramp was significantly higher than the speed limit. This research work provides ground truth data for deceleration lane design, driver ability training and off-ramp traffic safety management. [ABSTRACT FROM AUTHOR]