Automation is defined as the replacement of tasks previously handled by humans with machines [1]. One value of automated driving is that it enables humans to engage in non-driving tasks, thereby allowing for more efficient use of time. However, humans may suffer from boredom created by monotonous environment or drowsiness due to human characteristics in conditional driving automation, where the role of resuming DDTs is assigned to humans. Humans may have an intention of arriving at their destination faster. Humans may underestimate risks associated with drowsiness and may not form an intention to take a break. Humans may have their attention focused on non-driving tasks and fail to find an opportunity for taking a break. The automated driving system should not continue to vehicle control in a situation where humans, who are expected to act as a backup, feel drowsy. One technology that is currently lacking is a mechanism for humans and machines to communicate their intentions and form joint decisions for strategic, tactical, and operational levels. The mechanism is called mediation, meaning means of interaction strategies aimed to reach a joint intent and an adequate action. Against this background, the purpose of the study is to design human-machine mediation for joint intention formation in coping with drowsiness under driving automation. We constructed a state description of human-machine interaction based on relationship between environment, human intentions and behaviors, and machine intentions and behaviors. As shown in Fig. 1, the flow of interactions that make a joint intent and an adequate action for responding to driver drowsiness was described using Swimlane Diagrams [2]. The Karolinska Sleepiness Scale [3] was used to determine sleepiness levels, and opportunities for human-machine interaction were constructed according to sleepiness levels and levels of automation (LOA) [4]. In an era of increasing autonomy, technologies that support consensus-building between humans and machines are important, and the contribution of this study was to present a method for describing the state of human-machine interactions, using the use case of coping with drowsiness. We plan to evaluate the usability of the designed mediation in future experiments.

Machines, e.g. empowered by AI and based on virtual models, can help to improve the quality of life. To exploit this potential and also integrate this with the real world and society, cooperation and teaming of these machines with humans, and with societies is crucial. Human-Machine Patterns can be a key concept to analyze, understand, design, engineer and evaluate the delicate interplay of humans and machines. Key issues here are to understand in which situations which agents should do, want to do, can do, will do and finally actually do which actions, and who then is accountable. This overview article is intended as an introduction into the special session on Shared and Cooperative Control, especially on patterns and models for controllability and resilience. It is a direct follow-up on the 2022's special session and overview article (which is also available on IEEE Xplore). It introduces the topic of shared and cooperative control of human-machine and human-AI systems, especially in the light of the new advances in AI technology. This paper gives a short overview on the state of research on interaction patterns, controllability and resilience, before it focuses on the fundamental aspects of which actor should do, wants to do, can do, does and will be accountable for a pattern, sub-pattern or action within a pattern. Examples of what can be achieved with this basic architecture are given, e.g. for the recognition of intent or for the support by assistant systems, using the automotive domain as a first application example.

Partial driving automation systems execute sustained lateral and longitudinal control, while humans are required to supervise the automated driving features. Similar with drivers using manual driving settings, those using automated driving systems may also experience drowsiness. However, existing systems that aim to detect driver drowsiness tend to be unreliable. This study applies a dual-control scheme in the partial driving automation context in which the driver must keep their hands on the vehicle’s steering wheel. This scheme executes a partial steering control when a vehicle lane departure is anticipated (because of inappropriate torque input), and then, activates a deceleration control if the driver does not properly perform the required action. To determine whether the driver is supervising the partial driving automation, the scheme attempts to create an opportunity for driver–automation interactions. Thus, the controller’s objectives are twofold: safety control and driver state identification. This study investigated the effectiveness of the scheme for identifying driver drowsiness and preventing lane departures using only vehicle information. Twenty drivers participated in a fixed-base driving simulator experiment in a sleep-inducing environment. While we observed cases in which the system could effectively bring the vehicle to a controlled stop, the timeliness and accuracy of the driver state identification remained as issues owing to indirect links between the drivers’ drowsiness level and controller activation. We conclude that although the dual-control scheme is a useful mechanism to avoid lane departures, the driver state identification needs to be improved to ensure timely and effective detection of driver drowsiness.

Elderly drivers often tend to disobey stop signs, and the number of vehicle accidents associated with this is increasing. The problem we address in this work is that of failure (or inability) of elderly drivers to identify potential conflicts with other road users at stop-sign intersections. The purpose of the study is to investigate the influence of deceleration control with brake hold on the driving habits of elderly drivers in potentially hazardous situations. This study proposes a driver assistance system with three functionalities: 1) information provision to warn drivers that they are approaching a stop-sign intersection; 2) deceleration control to stop the vehicle; and 3) brake hold to ensure that the vehicle has stopped completely. The timeline for a stop-sign intersection scenario is divided into pre- and post-vehicle-stop phases. The effectiveness of the proposed approach in the context of braking-assistance intervention was evaluated by conducting a public-road driving experiment involving 34 elderly drivers. It was observed that the participants could be guided to exhibit rule-following driver behavior voluntarily. The proposed system increased safety for elderly drivers, not only by avoiding stop-sign violations, but also by increasing the available time for a driver to search for hidden hazards in blind spots. We conclude that the braking-assistance intervention system is effective in helping drivers avoid failure or inability to search for potential conflict owing to stop-sign violations.

In the context of hands-on partial driving automation, drivers engage in a combination of lane centering system (LCS) and adaptive cruise control (ACC). In lane change scenarios, what should the system do, if it detects the approach of vehicle in the blind spot? Humans with powerful and intelligent machine sometimes suffer from such as automation surprises and distrust in automation. By contrast, haptic and continuous information would enable the driver to virtually touch their potentially hazardous environment. In this study, we propose the haptic protection with an adaptive level of authority that can be turned based on risk indicators, i.e., TTC and THW , in the blind spot scenarios. In haptic guidance systems, the artificial stiffness around the controller’s desired steering angle is used as the level of haptic authority to guide the driver to normative behavior. On the other hand, in the proposed protection system, the artificial stiffness around the driver’s desired steering angle is used as the level of haptic authority to warn the driver about their erroneous action. Thus, the definition of the level of haptic authority (LoHA) is different between the guidance-type and protection-type in terms of the objective. In a simulation with a parameter space of lane change scenarios, this study highlighted the combined impact of the haptic guidance and haptic protection on the driver bahavior. Results indicated that the use of the proposed haptic protection can enable the drivers to recognize their inappropriate actions.

Expert drivers, which have a wealth of driving experience, can perceive deeper structures of the environment, and can adapt to a changing environment, and can therefore take a hazard-anticipatory driving behavior to increase safety margins. This study focuses on assessing the potential risk for a pedestrian who initiates a road crossing from the driver's blind areas, and we have developed an advanced driver assistance system with hazard-anticipatory driving intelligence. A purpose of this paper is to present the design of haptic guidance support for hazard-anticipatory driving. Three important design issues for risk predictive haptic guidance support system can be distinguished: (1) design of the one-pedal speed control interface, (2) design of the risk-prediction based driver model, and (3) design of the haptic feedback with an active gas pedal. Research question is that whether or not drivers can be led to hazard-anticipatory driving behavior through the risk predictive haptic guidance support to give his/her continuous potential risk information with respect to blind areas. Through a simulator experiment, we confirmed that the proposed ADAS assisted the drivers to guide the referenced velocity by using the stroke range designed for deceleration via the one-pedal speed control interface.

Autonomous Emergency Braking systems have been already introduced to the markets to realize road traffic zero fatalities, and humans positively evaluated the functions of such assistance system. However, such assistance system reaches its limit when there are unexpected moving obstacles appearing suddenly from poor visibility area, such as a corner of intersection, and a space behind a parked vehicle. This paper focuses on a parked vehicle overtaking scenario that an occluded pedestrian suddenly might intend to cross a road, and a risk predictive shared deceleration control system which can prepare and avoid for a hidden collision risk is proposed. Under a driving simulator experiment, we investigated its functionality and effectiveness of the proposed early intervention support, and we confirmed that the proposed assistance system can be effective for guiding the drivers to trace the desired velocity.

Autonomous emergency braking systems have been already introduced to the markets, and have good driver acceptance in terms of the functions of such system. However, such system reaches its limit when there are unexpected moving obstacles appearing suddenly from poor visibility area, such as a space behind a parked vehicle. This paper describes a shared control in risk predictive braking manoeuvre for preventing collision with a pedestrian, and constructs a protection-typed support with an active gas pedal and extension-typed support with a brake pedal. The assistance system activates in a hidden risk situation that an occluded pedestrian suddenly might intend to cross a road. Under a driving simulator experiment, we investigated its functionality and effectiveness of the proposed assistance system, and we confirmed that the proposed system was effective for guiding drivers to trace the desired safe velocity.

Driver drowsiness is a common cause of fatal traffic accidents. In this study, a driver assistance system with a dual control scheme is developed; it attempts to perform simultaneously the safety control of the vehicle and identification of the driver's state. The assistance system implements partial control in the event of lane departure and gives the driver the chance to voluntarily take the action needed. If the driver fails to implement the steering action needed within a limited time, the assistance system judges that “the driver's understanding of the given situation is incorrect” and executes the remaining control. We used a driving simulator equipped with the assistance system to investigate the effectiveness of identifying driver drowsiness and preventing lane departure accidents. Twenty students participated in three trials on a straight expressway, and they were required to implement only lateral control. We hypothesized that a participant cannot implement the action needed to maintain safety when he/she falls asleep, and that in such a case, the assistance system will implement the safety control repeatedly. The assistance system assisted the participants only when almost really needed, such as when their eyelids were closed. The results validated the hypothesis, showing that the assistance system implemented the safety control repeatedly when a participant fell asleep. In addition, the algorithms used by the assistance system to determine whether the driver can continue driving were evaluated through a leave-one-out cross-validation, and they were proven to be effective for identifying driver drowsiness.

This paper proposes a driver assistance system with a dual control theoretic scheme, which tries to perform safety control of a vehicle as well as identification of the driver's cognitive state simultaneously. The dual control theoretic driver assistance algorithm uses steering torque control based on a linear quadratic regulator (LQR). A controller and mathematical models for the vehicle and steering system were built, where the steering torque control was applied to switching feedback gains in the LQR controller. This paper discusses the dynamic characteristics for steering torque control under constraints on the state and manipulated variables.

This paper describes a 32-bit CISC superscalar microprocessor designed for high-end embedded applications, such as X-window terminals and printers. To realize high performance of processing a series of memory data such as a frame buffer and a character-string, we have used block-data-transfer and 64-bit processing to improve execution efficiency of multiple-operation instructions of the processor. 16-byte block read/write operations through a 32-bit external data bus have accomplished high bandwidth for DRAM access, even in the case of non-cacheable regions such as a frame buffer. In executing multiple-operation instructions, 64-bit processing has been realized by operating the dual execution circuits for superscalar executions and a 64-bit internal data bus under the control of a very long micro-instruction word. By using these techniques, we have enhanced the frame buffer manipulation performance of this processor 1.5 to 2 times.>

The sensor with real-time environment recognition ability is one of the key technologies for autonomous robots. The authors have designed and implemented a small size optical range sensor for their experimental mobile robot. The sensor consists of a laser slit generator, a CCD image sensor and a processing unit. Using this sensor, the real-time obstacle avoiding function is realized and added to the autonomous navigation aspect of the robot.>

Pipelining of instruction execution significantly improves computer performance, but high dependencies between instructions limit the maximum concurrency that is achievable by pipelining. A hardware scheme to avoid pipeline interlock delays caused by dependencies in address generation is proposed. This scheme is implemented in the 32-b microprocessor M32/100. The M32/100 has a hardware interlock mechanism with scoreboard registers and a working stack pointer that is modified prior to the execution of each instruction. A simulator has been written and several benchmarks have been executed to investigate the performance achieved by these schemes.>

The GMICRO/100, a 32-b microprocessor based on the TRON architecture specification, is described. The GMICRO/100 uses high-level instructions, such as those in bit-map manipulation. The bit-map instructions are implemented by pipelining micro-operations, and achieve optimum use of the memory bus in the execution. The bit-map instructions are 2 to 3 times faster than repeating move instructions by the software. Microprogram development tools for the GMICRO/100 design are described.>