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Attention Management in Highly Autonomous Driving
Published in Mustapha Mouloua, Peter A. Hancock, James Ferraro, Human Performance in Automated and Autonomous Systems, 2019
Carryl L. Baldwin, Ian McCandliss
Automobile crashes are a persistent threat to public safety and leading cause of death in the United States (NHTSA, 2018a). Human error and driver distraction play key roles in a high percentage of these crashes (Guo et al., 2017). Despite public safety campaigns, the number of alcohol-impaired driving fatalities remains high (29% in 2017, down 1.1% from 2016; NHTSA, 2018b). At the same time, the number of licensed drivers and vehicle miles traveled by drivers over the age of 65 has been steadily increasing as have the numbers of fatal crashes involving these drivers (NHTSA, 2018b). ADAS have the potential to decrease crashes for all roadway users (e.g., drivers, vehicle occupants, pedestrians, and bicyclists) and may be particularly helpful for high-crash-risk segments of the driving population (e.g., older drivers). However, effective design of these systems is urgently needed to realize these benefits in public safety.
Transportation
Published in Sara J. Czaja, Walter R. Boot, Neil Charness, Wendy A. Rogers, Designing for Older Adults, 2019
Sara J. Czaja, Walter R. Boot, Neil Charness, Wendy A. Rogers
Advanced Driver Assistance Systems (ADAS) have been implemented in many newer vehicles to reduce crash risk and improve driver comfort, and may help offset the effects of age-related perceptual and cognitive changes. ADAS includes collision warning, blind spot warning, intelligent cruise control, adaptive headlights, parking assistance, and navigation assistance systems. Many of these technologies can help keep older drivers safely on the road for longer (Eby et al., 2016). However, like all systems, they need to be designed to align with the preferences, needs, and abilities of older adults for their maximum benefit to be achieved. Poorly designed technology systems can take older adults’ attention from the roadway, potentially exacerbating rather than reducing driver risk and discomfort, and inhibiting the adoption and use of beneficial ADAS. The guidelines discussed in Chapter 7 are relevant to the design of in-vehicle technologies. For example, in driving simulator studies, there is a strong benefit to using both visual and auditory channels to issue alerts from such systems, reinforcing the earlier advice about using multiple input channels for warnings. It is also important to conduct usability studies (Chapter 5), and include older adults in the process (Chapter 6), and to provide older users with adequate training on the use of these technologies (Chapter 8). The same holds true for not just ADAS systems on the market now, but future systems and vehicles in which even more driving tasks are assigned to the vehicle (i.e., semi-autonomous and autonomous automobiles).
An Embedded Implementation of a Traffic Light Detection System for Advanced Driver Assistance Systems
Published in Om Prakash Jena, Sudhansu Shekhar Patra, Mrutyunjaya Panda, Zdzislaw Polkowski, S. Balamurugan, Industrial Transformation, 2022
Riadh Ayachi, Mouna Afif, Yahia Said, Abdessalem Ben Abdelali
Pedestrian and vehicle safety is a significant area that automotive companies are focusing on heavily. Recently, automotive manufacturers have developed many technologies to help prevent accidents. These technologies allowed the automation and enhancement of the vehicular system to assist the driver and ensure its safety. Advanced driver assistance systems (ADAS) [2] are one of the automotive technologies established by Industry 4.0. ADAS is a combination of smart systems that facilitate the control of the vehicle by the driver and can perform easy and repetitive tasks such as parking, highway driving, and cruise control. ADAS collect different sensor data and use the most recent artificial intelligence techniques.
A survey on knowledge and perceptions of advanced driver assistance systems in Massachusetts drivers
Published in Traffic Injury Prevention, 2022
Apoorva Pramod Hungund, Anuj K. Pradhan
Advanced Driver Assistance Systems (ADAS) are mainly used to increase safety and comfort while driving. Under specific circumstances and within certain conditions, these features can provide driver support and assistance. Although they may handle certain parts of the driving task, ADAS systems cannot independently handle driving (SAE 2021). Drivers with incomplete or incorrect knowledge of these systems may end up using systems outside of their operational capability. Jenness et al. (2008) found that almost 72% of participants using Adaptive Cruise Control (ACC) are unaware of its limitations. It is also possible that drivers with incomplete knowledge of ADAS do not use these features to their full extent. Furthermore, additional factors such as trust and perceptions (Beggiato and Krems 2013; DeGuzman and Donmez 2021), experience (Larsson 2012), and age (Crump et al. 2016) may affect ADAS knowledge and use. To this end, we conducted an online survey to understand the extent to which ADAS features are actually used and to examine the knowledge drives have ADAS. For this study, we specifically focused on Adaptive Cruise Control (ACC) and Lane Keeping Assist (LKA). This survey assessed ACC and LKA knowledge among experienced and inexperienced users of ADAS, explicitly focusing on drivers of Massachusetts.
A 3D body posture analysis framework during merging and lane-changing maneuvers
Published in Journal of Transportation Safety & Security, 2018
A. Kondyli, A. Barmpoutis, V. P. Sisiopiku, L. Zhang, L. Zhao, M. M. Islam, S. S. Patil, S. Rostami Hosuri
Given that driver error constitutes the primary reason for the majority of traffic crashes, elimination of this factor through the adoption of autonomous and connected vehicle technology is expected to improve traffic safety. The US Department of Transportation's (USDOT) connected-vehicle research program uses technologies such as advanced wireless communications, on-board computer processing, advanced vehicle-sensors, global positioning system (GPS) navigation, and smart infrastructure, to identify imminent road hazards and warn the drivers accordingly (USDOT, 2011). A number of crash avoidance systems have been established to date, such as emergency stop warning, forward collision warning, intersection movement assistance, blind-spot and lane-change warning, and do not pass warning. Additional advanced (or intelligent) driver assistance systems (ADAS) designed to provide added traffic safety are already in place (Shaout, Colella, & Awad, 2011). These systems do not involve intervehicle communication and are designed to provide assistance or warning to drivers by considering the longitudinal position of the vehicle or other vehicle-related components. Examples of ADAS applications include automatic parking, adaptive light control, night vision, lane-change assistance, traffic-sign recognition, collision-avoidance system, lane-departure warning system, and hill-descent control. Apart from these systems that focus on the vehicle, there are limited systems already in place that are designed to monitor the driver's eye movement for inattention or drowsiness using light-emitting diode (LED) sensors.
Robust design optimisation of adaptive cruise controller considering uncertainties of vehicle parameters and occupants
Published in Vehicle System Dynamics, 2020
Hansu Kim, Tae Hee Lee, Yuho Song, Kunsoo Huh
In recent years, interest in autonomous vehicle has been increasing in automotive industry, and research on advanced driver assist systems (ADAS), which comprise the previous stage of autonomous driving, is being actively performed. In 2013, the global ADAS market was valued at $14.80 billion, and it is expected to increase to $50.49 billion in 2020 [1]. ADAS include adaptive cruise control (ACC), lane keeping assist, autonomous emergency braking, etc. ACC occupies the largest segment, approximately 18.15%, among ADAS [1]. Moreover, since global environmental regulations such as fuel efficiency and CO2 emissions are being strengthened, fuel efficiency is notably issued.