According to SAE J3016, autonomous driving can be divided into six levels, and partially automated driving is possible from level three up. A partially or highly automated vehicle can encounter situations involving total system failure. Here, we stud...
With the rapid adoption of automated driving systems, ensuring safe and efficient driver takeover has become a crucial challenge for road safety. This study introduces a novel psychological framework for understanding and predicting takeover behavior...
This paper presents a Threat Analysis and Risk Assessment (TARA) of the takeover request (TOR) component in Advanced Driver Assistance Systems (ADAS) for SAE Level 2–3 automation. A TOR prompts the human driver to retake control when the system...
A new concept in the interior design of autonomous vehicles is rotatable or swivelling seats that allow people sitting in the front row to rotate their seats and face backwards. In the current study, we used a take-over request task conducted in a fi...
Take-over time is a critical factor affecting safety. Accurately predicting the take-over time provides a more reliable basis on issuing take-over requests, assessment of take-over risks, and optimization of human–machine interaction modes. Alt...
In highly automated driving systems (ADSs), drivers’ ability to resume manual driving remains a road safety issue. However, to the best of our knowledge, there is no existing computational model to predict which drivers require more than the 4...
Ensuring the driver’s readiness to take over before a takeover request is issued by an autonomous driving system is crucial for a safe takeover. However, current takeover prediction models suffer from poor prediction accuracy and do not conside...
Autonomous vehicles are the near future of the automobile industry. However, until they reach Level 5, humans and cars will share this intermediate future. Therefore, studying the transition between autonomous and manual modes is a fascinating topic....
Current autonomous vehicles require human drivers to take over control during emergencies or in environments the system cannot handle. During other periods, drivers are permitted to engage in non-driving-related tasks. It is essential to investigate...
Autonomous driving systems relieve drivers from continuous vehicle operation and constant monitoring, allowing them to engage in non-driving-related tasks (NDRTs). However, immersion in such tasks can impair drivers’ perception of both the take...
To address the challenges of recognition lag and limited representational capacity of single-modal physiological information in driver intention recognition under human–machine co-driving environments, this study constructed a highway driving t...
This paper investigates the effects of non-driving related tasks, take-over request time, and take-over mode interactions on take-over performance in human–machine cooperative driving in a highway environment. Based on the driving simulation pl...
We investigated the effect of driver posture on driving control following a takeover request (TOR) from autonomous to manual driving in level 3 autonomous vehicles. When providing a TOR, driving behaviors need to be investigated to develop driver mon...
21 October 2021
Drivers’ take-over intention is important for the design of the automated driving systems and affects the safety of automated driving. This study explored the influence factors on drivers’ take-over intention during conditionally automated driving, e...
Level 3 automated driving necessitates rapid driver reengagement following a takeover request, making human–machine interface design critical for safety. As a sensor-based in-vehicle interface, the augmented reality head-up display (AR-HUD) int...
During unexpected driving situations in autonomous vehicles, such as a system failure, the driver should take over control from the vehicles in SAE Level 3 to cope with unexpected situations. Therefore, it is necessary to develop reasonable takeover...
In the context of automated vehicles, transparency of in-vehicle intelligent agents (IVIAs) is an important contributor to driver perception, situation awareness (SA), and driving performance. However, the effects of agent transparency on driver perf...
Research on the influence of age on various automated driving conditions will contribute to an understanding of driving behavior characteristics and the development of specific automated driving systems. This study aims to analyze the relationship be...
Takeover safety remains a critical human-factors issue in conditionally automated driving because drivers must resume manual control under limited time and varying traffic conditions. This study examined how auditory alert urgency influences takeover...
The driver’s perception level and takeover performance are two major factors that result in accidents in autonomous vehicles. This study’s goal is to analyze the change in drivers’ perception level and its influence on takeover perf...
Transitions of control between automated driving systems and human drivers remain safety-relevant and cognitively demanding moments in human–automation interaction. Recent studies show that transition performance depends not only on takeover ti...
The transition to autonomous driving is creating mixed traffic environments in which human-driven vehicles, partially automated vehicles, and autonomous vehicles must continuously interact, adapt, and respond to one another. This paper presents a com...
With the increasing prevalence of autonomous vehicles (AVs), drivers’ spatial cognition and takeover performance have become critical to traffic safety. This study investigates the effects of landmark salience—specifically visual and stru...
31 December 2021
With the popularization and application of conditionally automated driving systems, takeover requirements are becoming more and more frequent, and the subsequent takeover safety problems have attracted attention. The present study used functional mag...
Drivers of L3 automated vehicles (AVs) are not required to continuously monitor the AV system. However, they must be prepared to take over when requested. Therefore, it is necessary to design an in-vehicle environment that allows drivers to adapt the...
Grassland highways present a distinctive application context for human–machine co-driving (HMCODR), yet the evidence on takeover behavior in such environments remains limited. This study employed a driving simulator experiment to investigate ho...
Vehicle automation is linked to various benefits, such as increase in fuel and transport efficiency as well as increase in driving comfort. However, automation also comes with a variety of possible downsides, e.g., loss of situational awareness, loss...
9 February 2023
Nowadays, conditional automated driving vehicles still need drivers to take-over in the scenarios such as emergency hazard events or driving environments beyond the system’s control. This study aimed to explore the changing trend of the drivers...
The aim of this study was to compare the success of two different Human Machine Interfaces (HMIs) in attracting drivers’ attention when they were engaged in a Non-Driving-Related Task (NDRT) during SAE Level 3 driving. We also assessed the valu...
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 iss...
Level 3 automated vehicles (L3 AVs) enable the driver to perform non-driving tasks, taking over in an emergency. In recent years, studies have extensively discussed the influencing factors of L3 AV takeovers. Extensive literature review shows that L3...
The development of highly automated driving requires dynamic approaches that anticipate the cognitive state of the driver. In this paper, a cognitive model is developed that simulates a spectrum of cognitive processing and the development of situatio...
In recent years, the takeover process of conditional automated driving has attached a great deal of attention. However, most of the existing research has focused on the effects of human-machine interactions or driver-related features (e.g., non-drivi...
Highways are one of the most suitable scenarios for automated driving technology. For conditionally automated driving, drivers are required to take over the vehicle when the system reaches its boundary. Therefore, it is necessary to evaluate the driv...
In automated driving, the user interface plays an essential role in guiding transitions between automated and manual driving. This literature review identified 25 studies that explicitly studied the effectiveness of user interfaces in automated drivi...
In conditionally automated driving, the engagement of non-driving activities (NDAs) can be regarded as the main factor that affects the driver’s take-over performance, the investigation of which is of great importance to the design of an intell...
26 October 2022
The objective of this study is to examine the effects of visibility and time headway on the takeover performance in L3 automated driving. Both non-critical and critical driving scenarios were considered by changing the acceleration value of the leadi...
In the development of highly automated driving systems (L3 and 4), much research has been done on the subject of driver takeover. Strong focus has been placed on the takeover quality. Previous research has shown that one of the main influencing facto...
The advent of autonomous vehicles (AV) could revolutionize the automotive industry by significantly improving safety, efficiency, and accessibility. Despite their potential to improve traffic safety by reducing human error, their integration into exi...
Automated driving technologies are rapidly being developed. However, until vehicles are fully automated, the control of the dynamic driving task will be shifted between the driver and automated driving system. This paper aims to explore how transitio...
6 February 2022
There are many factors that will influence the workload of drivers during autonomous driving. To examine the correlation between different factors and the workload of drivers, the influence of different factors on the workload variations is investiga...
When a conditionally automated vehicle controlled by the machine faces situations beyond the capability of the machine, the human driver is requested to take over the vehicle. This study aims to assess the short-term effects of three factors on the t...
Partially automated driving (PAD, Society of Automotive Engineers (SAE) level 2) features provide steering and brake/acceleration support, while the driver must constantly supervise the support feature and intervene if needed to maintain safety. PAD...
High-speed trains are some of the most important transportation vehicles requiring human–computer collaboration. This study investigated the effects of different types of icons on recognition performance and cognitive load during frequent obser...
With the increasing emergence of intelligent vehicles, novel accident patterns have gradually emerged. In human–machine cooperative driving (HMCD) states, despite driving automation systems being capable of controlling lateral and longitudinal...
In conditionally automated driving, a vehicle issues a take-over request when it reaches the functional limits of self-driving, and the driver must take control. The key driving parameters affecting the quality of the take-over (TO) process have yet...
In the realm of conditionally automated driving, understanding the crucial transition phase after a takeover is paramount. This study delves into the concept of post-takeover stabilization by analyzing data recorded in two driving simulator experimen...
Conditional automated driving delegates routine control to automation while keeping drivers responsible for supervision and timely takeovers. In this context, safety and usability hinge on calibrated trust, a state between overtrust and distrust that...
Most studies on users’ perception of highly automated driving functions are focused on first contact/single usage. Nevertheless, it is expected that with repeated usage, acceptance and usage of automated driving functions might change this perc...
Semi-autonomous vehicles (AVs) enable drivers to engage in non-driving tasks but require them to be ready to take control during critical situations. This “out-of-the-loop” problem demands a quick transition to active information processi...
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