Real-Time Lane Detection and Tracking for Advanced Driver Assistance Systems

Bilal, Hazrat; Yin, Baocai; Khan, Jawad; Wang, Luyang; Zhang, Jing; Kumar, Aakash

doi:10.23919/chicc.2019.8866334

Cited by 92 publications

(3 citation statements)

References 23 publications

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“…Subsequent research should assess OSH effects of real-time image processing in driving scenarios ( 56 ), which comprise cognitive load and potential distractions ( 57 , 67 ). Quantitative measures (e.g., eye-tracking and physiological monitoring) provide insights into the mental workload and attention demands ( 68 ). Besides, a focus should be placed on mitigating potential negative OSH effects ( 69 ).…”

Section: Discussionmentioning

confidence: 99%

Machine learning-based cognitive load prediction model for AR-HUD to improve OSH of professional drivers

Teng

Wan

Kong

et al. 2023

Front. Public Health

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MotivationAugmented reality head-up display (AR-HUD) interface design takes on critical significance in enhancing driving safety and user experience among professional drivers. However, optimizing the above-mentioned interfaces poses challenges, innovative methods are urgently required to enhance performance and reduce cognitive load.DescriptionA novel method was proposed, combining the IVPM method with a GA to optimize AR-HUD interfaces. Leveraging machine learning, the IVPM-GA method was adopted to predict cognitive load and iteratively optimize the interface design.ResultsExperimental results confirmed the superiority of IVPM-GA over the conventional BP-GA method. Optimized AR-HUD interfaces using IVPM-GA significantly enhanced the driving performance, and user experience was enhanced since 80% of participants rated the IVPM-GA interface as visually comfortable and less distracting.ConclusionIn this study, an innovative method was presented to optimize AR-HUD interfaces by integrating IVPM with a GA. IVPM-GA effectively reduced cognitive load, enhanced driving performance, and improved user experience for professional drivers. The above-described findings stress the significance of using machine learning and optimization techniques in AR-HUD interface design, with the aim of enhancing driver safety and occupational health. The study confirmed the practical implications of machine learning optimization algorithms for designing AR-HUD interfaces with reduced cognitive load and improved occupational safety and health (OSH) for professional drivers.

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Section: Discussionmentioning

confidence: 99%

Machine learning-based cognitive load prediction model for AR-HUD to improve OSH of professional drivers

Teng

Wan

Kong

et al. 2023

Front. Public Health

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“…We started by defining control variables that directly relate to the energy efficiency and livability of intelligent buildings. The main components of these variables are new energy, which is simply another way of saying renewable energy sources, energy delivery temperature, which is the thermal energy distribution across the building for various purposes, and water supply temperature, which is simply another way of saying the temperature of the water provided for various purposes [31,32]. On the basis of these defined variables, data is collected from various IoT devices, such as sensors, actuators, and control systems, and it is ensured that the data collected is correct, reliable, and related to the building's energy usage.…”

Section: Energy Optimization Of Intelligent Building By Using Niche G...mentioning

confidence: 99%

Enhancing Energy Efficiency by Improving Internet of Things Devices Security in Intelligent Buildings via Niche Genetic Algorithm-Based Control Technology

Liang,

Xing,

2023

Applied Sciences

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The security measures of IoT devices used in intelligent buildings are one of the ways by which energy efficiency can be accomplished. IoT devices are very important for data collecting and monitoring in intelligent buildings, but a lack of security could result in errors in energy consumption decisions that result in energy waste. To ensure the success of the control systems used for energy optimization, it is necessary to address the security of IoT devices in order to avoid illegal access, data manipulation, and disruptions. This work proposes a research idea and scheme for energy-saving optimization of intelligent buildings by assuring the security of IoT devices used in intelligent buildings. First of all, we defined several parameters that are related to IoT devices’ security, energy consumption, and occupant comfort in the intelligent building environment. Secondly, we collected data for each of these parameters by utilizing IoT devices such as actuators, sensors, and other control systems. The niche genetic algorithm (NGA) refers to a particular class of genetic algorithms that is used to tackle problems involving many optimization objectives. We focused on optimizing both energy consumption and occupants’ comfort; therefore, we used an NGA for the preprocessed data with the goal of evaluating the data for the purpose of ensuring the comfort of occupants and protection of the security of IoT devices, which eventually leads to energy optimization. Finally, the results of the proposed approach are analyzed and carefully compared with earlier work, demonstrating that our proposed approach is significantly more effective and energy-optimized than earlier approaches. The results show that the total power consumption of the intelligent building system after using our proposed model is generally reduced by more than 18% compared with that before optimization, which shows that the intelligent building system-adaptive optimization control model can effectively optimize the operating parameters of the energy-saving system and achieve the security of IoT devices.

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“…pannagger@student.tugraz.at, demin.nalic@tugraz.at, faris.orucevic@tugraz.at, arno.eichberger@tugraz.at Branko Rogic 5 is with ADAS Simulation Department at MAGNA Steyr Fahrzeugtechnik AG & CO KG. branko.rogic@magna.com [16]- [20]. Many calculations and decisions like the driving lane computation or target detection, are based on the result of the detected lane markings (LM) or road boundaries.…”

Section: Introductionmentioning

confidence: 99%

Advanced Lane Detection Model for the Virtual Development of Highly Automated Functions

Pannagger¹,

Nalic²,

Faris³

et al. 2021

Preprint

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Virtual development and prototyping has already become an integral part in the field of automated driving systems (ADS). There are plenty of software tools that are used for the virtual development of ADS. One such tool is CarMaker from IPG Automotive, which is widely used in the scientific community and in the automotive industry. It offers a broad spectrum of implementation and modelling possibilities of the vehicle, driver behavior, control, sensors, and environmental models. Focusing on the virtual development of highly automated driving functions on the vehicle guidance level, it is essential to perceive the environment in a realistic manner. For the longitudinal and lateral path guidance line detection sensors are necessary for the determination of the relevant perceiving vehicle and for the planning of trajectories. For this purpose, a lane sensor model was developed in order to efficiently detect lanes in the simulation environment of CarMaker. The so-called advanced lane detection model (ALDM) is optimized regarding the calculation time and is for the lateral and longitudinal vehicle guidance in CarMaker.

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Real-Time Lane Detection and Tracking for Advanced Driver Assistance Systems

Cited by 92 publications

References 23 publications

Machine learning-based cognitive load prediction model for AR-HUD to improve OSH of professional drivers

Machine learning-based cognitive load prediction model for AR-HUD to improve OSH of professional drivers

Enhancing Energy Efficiency by Improving Internet of Things Devices Security in Intelligent Buildings via Niche Genetic Algorithm-Based Control Technology

Advanced Lane Detection Model for the Virtual Development of Highly Automated Functions

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