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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.
AI for Advanced Driver Assistance Systems
Published in Josep Aulinas, Hanky Sjafrie, AI for Cars, 2021
Traffic sign recognition has become widely available in cars nowadays; thanks to strong incentives from Euro NCAP and others, it may even become standard equipment in new cars someday. Yet already now it is possible for older cars to be retrofitted with a camera-based ADAS aftermarket device in order to take advantage of speed limit information, collision warning and other ADAS features. Due to the lack of integration with the vehicle, however, these devices are limited to warning functions and cannot actively control the vehicle.
Introduction to Artificial Intelligence and Biometrics Applications
Published in Rodgers Waymond, Artificial Intelligence in a Throughput Model, 2020
These Artificial Intelligence systems can utilize previous encounters to inform future decisions. Some of the decision-making functions in driverless self-driving automobiles (oftentimes referred to as an autonomous car/driverless car) are designed in this manner. A self-driving car is a vehicle that utilizes a combination of sensors, cameras, radar and Artificial Intelligence in order to travel between destinations without a human operator. To qualify as fully autonomous, a vehicle must be able to navigate without human involvement to a prearranged destination over streets and lanes that have not been modified for its function. Further, observations inform actions occurring in the near future, such as a car changing lanes. These observations are not stored permanently. Moreover, the US National Highway Traffic Safety Administration (NHTSA) (https://whatis.techtarget.com/definition/National-Highway-Traffic-Safety-Administration-NHTSA) has put down six levels of automation, beginning with zero, where humans do the driving, through technologies up to fully autonomous cars. Driver assistance, also known as advanced driver-assistance systems (ADAS), are technologies implemented to craft motor vehicle travel safer by automating, improving or adapting some or all of the tasks involved in operating a vehicle.
Identifying crashes potentially affected by conditionally automated vehicles in Finland
Published in Journal of Intelligent Transportation Systems, 2023
Fanny Malin, Anne Silla, Johannes Mesimäki, Satu Innamaa, Harri Peltola
The European target to halve by 2020 the number of road traffic fatalities that occurred in 2010 is highly unlikely to be reached, as the latest available statistics show the average reduction from 2010 to 2018 to have been a mere 21% (EC, 2020a). In 2017, the same target was reaffirmed for 2030 with respect to 2020, this time including also serious injuries (Valletta Declaration on Road Safety, 2017). Developments in sensor and camera technologies have produced Advanced Driver Assistance Systems (ADAS) to assist drivers with driving and parking tasks. Some ADAS, such as Intelligent Speed Adaptation (ISA) and Autonomous Emergency Braking (AEB), can even intervene in the driving task if necessary. A growing number of ADAS are already appearing in today’s vehicles, either as factory fit or retrofit, some of which will become mandatory in new vehicles in 2022 (Scholliers et al., 2020). Based on several estimates, ADAS have helped reduce traffic crashes and have great potential to further improve traffic safety (Furlan et al., 2020; Scholliers et al., 2020).
Development of AEB control strategy for autonomous vehicles on snow-asphalt joint pavement
Published in International Journal of Crashworthiness, 2022
Xinqun Wang, Jianhua Wang, Weiyi Sun, Yuncheng Wang, Fei Xie, Dongni Guo
In recent years, intelligent driving assistance systems have been proposed to actively improve the safety performance of vehicles [6–9]. The autonomous emergency braking (AEB) system, also referred to as advanced or automatic emergency braking or collision-imminent braking, is a typical advanced driver assistance system that offers audio or visual warnings and applies the vehicle brakes when a collision is imminent [10–13]. A car fitted with such a system can react faster and more adequately than the driver during a frontal collision [14–16], so that gains benefits in collision warning [17], and works well in decreasing front to rear-end collisions [18]. A research estimating the benefit of AEB for vehicle–pedestrian crashes in the United States indicated that though not all crashes could be avoided, AEB significantly mitigated risk to pedestrians [19]. As a result, carmakers such as Volvo, Mercedes, Audi, and Lexus have placed increasing focus on the development of such systems.
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.