Automated Driving: Interactive Automation Control System to Enhance Situational Awareness in Conditional Automation

Olaverri-Monreal, Cristina; Kumar, Satyarth; Díaz-Álvarez, Alberto

doi:10.1109/ivs.2018.8500367

Cited by 18 publications

(6 citation statements)

References 17 publications

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“…In addition to the estimation of takeover readiness, the system is responsible for keeping the driver constantly aware of the situation both inside and outside of the vehicle. An Interactive Automation Control System (IACS) designed in [59] keeps the driver aware of the TOR on a display. Experiment results show that the response time to TOR and the total number of collisions decreases due to support from this system.…”

Section: Take-over Readiness Evaluationmentioning

confidence: 99%

Artificial Intelligence Methods in In-Cabin Use Cases: A Survey

Han

Hellert

et al. 2021

Preprint

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As interest in autonomous driving increases, efforts are being made to meet requirements for the high-level automation of vehicles. In this context, the functionality inside the vehicle cabin plays a key role in ensuring a safe and pleasant journey for driver and passenger alike. At the same time, recent advances in the field of artificial intelligence (AI) have enabled a whole range of new applications and assistance systems to solve automated problems in the vehicle cabin. This paper presents a thorough survey on existing work that utilizes AI methods for use-cases inside the driving cabin, focusing, in particular, on application scenarios related to (1) driving safety and (2) driving comfort. Results from the surveyed works show that AI technology has a promising future in tackling in-cabin tasks within the autonomous driving aspect.

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Section: Take-over Readiness Evaluationmentioning

confidence: 99%

Artificial Intelligence Methods in In-Cabin Use Cases: A Survey

Han

Hellert

et al. 2021

Preprint

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“…After detecting an upcoming TOR, an HMI conveys a timely message to the driver. The interaction needs to be dynamic depending on the urgency of the request, the message being in a prominent location within the vehicle [4,55] to attract the driver's attention. The way of conveying the message can be classified as follows:…”

Section: Tor Design Conceptsmentioning

confidence: 99%

“…As previously mentioned, the dynamic TOR process is triggered in emergency situations or in situations where the ODD boundary is predicted to be reached. As a consequence, the transition time of vehicle control from automated to manual driving mode, the so-called handover phase, is critical as a sufficiently comfortable transition time is necessary to guarantee road safety [55,90].…”

Section: Take Over-related Definitionsmentioning

confidence: 99%

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Automated Driving: A Literature Review of the Take over Request in Conditional Automation

Morales-Alvarez

Sipele

Léberon³

et al. 2020

Electronics

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In conditional automation (level 3), human drivers can hand over the Driving Dynamic Task (DDT) to the Automated Driving System (ADS) and only be ready to resume control in emergency situations, allowing them to be engaged in non-driving related tasks (NDRT) whilst the vehicle operates within its Operational Design Domain (ODD). Outside the ODD, a safe transition process from the ADS engaged mode to manual driving should be initiated by the system through the issue of an appropriate Take Over Request (TOR). In this case, the driver’s state plays a fundamental role, as a low attention level might increase driver reaction time to take over control of the vehicle. This paper summarizes and analyzes previously published works in the field of conditional automation and the TOR process. It introduces the topic in the appropriate context describing as well a variety of concerns that are associated with the TOR. It also provides theoretical foundations on implemented designs, and report on concrete examples that are targeted towards designers and the general public. Moreover, it compiles guidelines and standards related to automation in driving and highlights the research gaps that need to be addressed in future research, discussing also approaches and limitations and providing conclusions.

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“…In a real road situation the automation contributes to a decrease in driving workload and a consequent reduction in driver situational awareness, which needs to be taken into account when a vehicle control is expected from the driver. Therefore, in high automation driving, Driver State Monitoring Systems (DSMS) play a crucial role in determining whether or not the driver is prepared to take control of the vehicle and to respond to the TOR in the most appropriate manner [8], [9]. An example of DSMS that is employed in some vehicles is a signal that is automatically activated if the driver does not have their hands on the steering wheel for a certain period of time.…”

Section: Introductionmentioning

confidence: 99%

Vehicle Automation Field Test: Impact on Driver Behavior and Trust

Morales-Alvarez

Smirnov

Matthes

et al. 2020

2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC)

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With the growing technological advances in autonomous driving, the transport industry and research community seek to determine the impact that autonomous vehicles (AV) will have on consumers, as well as identify the different factors that will influence their use. Most of the research performed so far relies on laboratory-controlled conditions using driving simulators, as they offer a safe environment for testing advanced driving assistance systems (ADAS). In this study we analyze the behavior of drivers that are placed in control of an automated vehicle in a real life driving environment. The vehicle is equipped with advanced autonomy, making driver control of the vehicle unnecessary in many scenarios, although a driver take over is possible and sometimes required. In doing so, we aim to determine the impact of such a system on the driver and their driving performance. To this end road users' behavior from naturalistic driving data is analyzed focusing on awareness and diagnosis of the road situation. Results showed that the road features determined the level of visual attention and trust in the automation. They also showed that the activities performed during the automation affected the reaction time to take over the control of the vehicle.

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Automated Driving: Interactive Automation Control System to Enhance Situational Awareness in Conditional Automation

Cited by 18 publications

References 17 publications

Artificial Intelligence Methods in In-Cabin Use Cases: A Survey

Artificial Intelligence Methods in In-Cabin Use Cases: A Survey

Automated Driving: A Literature Review of the Take over Request in Conditional Automation

Vehicle Automation Field Test: Impact on Driver Behavior and Trust

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