How does the connected AR-HUD warning system affect lane-changing strategies during freeway abandoned object events:...

In the connected environment, Head-Up Displays (HUD) and Augmented Reality Head-Up Displays (AR-HUD) are anticipated to enhance driver performance by presenting warning information within the driver's line of sight. However, in freeway abandoned object events characterized by high suddenness and difficulty in advance prediction, it remains unclear whether both can effectively assist drivers in formulating optimal lane-changing strategies. This study aims to explore the impact mechanism of HUD and AR-HUD on drivers' lane-changing strategies in this scenario, and to identify the key factors affecting their effectiveness. To this end, a driving simulation platform was used to construct a scenario of connected freeway abandoned object event and design three warning systems (Baseline/HUD/AR-HUD). A total of 35 subjects' driving behavior data were collected through driving simulation experiments. Hazard perception time (HPT), lane-changing maneuver time (LMT), and time to collision (TTC) were selected as key indicators for lane-changing strategy from the perception and decision stage, risk-avoidance manipulation stage, and risk-avoidance result stage, respectively. Survival analysis, including Kaplan-Meier (KM) and Accelerated Failure Time (AFT) methods, were used to analyze the differences in HPT, LMT, and TTC among three warning systems (Baseline/HUD/AR-HUD) and to explore the effects of initial speed and driver attributes (gender, age, driving experience, and occupation) on lane-changing strategies in different warning system groups. The consistent results of KM and AFT indicate that in freeway abandoned object events, HUD did not show significant advantages compared to Baseline, while AR-HUD helped drivers achieve optimal lane-changing strategies. Specifically, the AR-HUD warning system can help drivers identify suddenly falling objects more quickly, perform low-speed and smooth lane-changing maneuvers, and significantly improve the final TTC. Additionally, AFT results further reveal that the effectiveness of the HUD system exhibited higher instability and stronger dependency on individual driver attributes, while AR-HUD demonstrated more consistent and robust effects across different driver groups. In particular, compared to the Baseline and HUD, the AR-HUD warning system reduced the differences in HPT among drivers with varying initial speeds and ages. While professional drivers maintain a tendency to perform faster lane-changing maneuvers in both the HUD and AR-HUD groups, both warning systems effectively prolonged their lane-changing maneuver time, with AR-HUD exhibiting a particularly pronounced effect. The HUD warning system has an adverse effect on the collision avoidance safety of drivers with high driving experience. In contrast, AR-HUD positively influenced drivers across all levels of driving experience, although drivers with low driving experience and high initial speeds still exhibited relatively lower TTC. The research results can provide references for the extended application of the AR-HUD warning system and the optimized design of the human-machine interaction system in intelligent connected vehicles.