Autonomous Emergency Brake (AEB) for Pedestrian for ASEAN NCAP Safety Rating Consideration: A Review

Baharuddin, M.I.; Khamis, Nor Kamaliana; Kassim, Khairil Anwar Abu; Mansor, Mohd Radzi Abu

doi:10.56381/jsaem.v3i1.110

Cited by 8 publications

(13 citation statements)

References 9 publications

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“…Vehicle Detection [34][35][36][37][38][39][40][41][42][43][44][45] Traffic Sign and Light Recognition [46][47][48][49][50][51][52][53][54][55][56][57][58][59] Pedestrian Detection [60][61][62][63][64][65][66][67][68][69][70][71][72][73][74][75][76][77][78] Lane Detection and Tracking [44, Traffic Scene Analysis [55,[102][103][104][105][10...…”

Section: Perceptionmentioning

confidence: 99%

“…In 2016, a total of 6000 pedestrians were killed, which is a record in the last three decades [58]. Moreover, based on vulnerable road users in the ASEN region, 13% of the deaths on roads are related to pedestrians [59]. In order to prevent deaths, the detection and localization of pedestrians have become a major focus of the study of autonomous vehicles.…”

Section: Pedestrian Detectionmentioning

confidence: 99%

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Vision-Based Autonomous Vehicle Systems Based on Deep Learning: A Systematic Literature Review

2022

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In the past decade, autonomous vehicle systems (AVS) have advanced at an exponential rate, particularly due to improvements in artificial intelligence, which have had a significant impact on social as well as road safety and the future of transportation systems. However, the AVS is still far away from mass production because of the high cost of sensor fusion and a lack of combination of top-tier solutions to tackle uncertainty on roads. To reduce sensor dependency and to increase manufacturing along with enhancing research, deep learning-based approaches could be the best alternative for developing practical AVS. With this vision, in this systematic review paper, we broadly discussed the literature of deep learning for AVS from the past decade for real-life implementation in core fields. The systematic review on AVS implementing deep learning is categorized into several modules that cover activities including perception analysis (vehicle detection, traffic signs and light identification, pedestrian detection, lane and curve detection, road object localization, traffic scene analysis), decision making, end-to-end controlling and prediction, path and motion planning and augmented reality-based HUD, analyzing research works from 2011 to 2021 that focus on RGB camera vision. The literature is also analyzed for final representative outcomes as visualization in augmented reality-based head-up display (AR-HUD) with categories such as early warning, road markings for improved navigation and enhanced safety with overlapping on vehicles and pedestrians in extreme visual conditions to reduce collisions. The contribution of the literature review includes detailed analysis of current state-of-the-art deep learning methods that only rely on RGB camera vision rather than complex sensor fusion. It is expected to offer a pathway for the rapid development of cost-efficient and more secure practical autonomous vehicle systems.

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Section: Perceptionmentioning

confidence: 99%

Section: Pedestrian Detectionmentioning

confidence: 99%

Vision-Based Autonomous Vehicle Systems Based on Deep Learning: A Systematic Literature Review

2022

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“…To date, ASEAN NCAP has introduced more safety assists technologies into their test protocols including the Blind Spot Technology (BST), Autonomous Emergency Braking (AEB), Lane Departure Warning, Forward Collision Warning, and many more. These technologies are introduced not only to save the passenger cars occupants but to also help avoid road collisions with other vehicles especially motorcycles (Baharuddin et al, 2019;Zaman et al, 2020). ASEAN NCAP has facilitated JPJ to mandate the fitment of relevant safety technologies on new vehicles and encouraged the consumers to choose safer cars.…”

Section: Asean Ncap's Influence On the Fitment Of Safety Technologies...mentioning

confidence: 99%

“…This system includes FCW where it will warn the driver before automated braking is applied. This AEB is essential to help the driver brake in time and avoid a collision from happening especially with a motorcycle in mixed traffic (Baharuddin et al, 2019).…”

Section: Autonomous Emergency Braking (Aeb)mentioning

confidence: 99%

A Review of Motorcycle Safety Technologies from the Motorcycle and Passenger Car Perspectives

Khalid¹,

Zulkipli²,

Solah³

et al. 2021

JSAEM

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Motorcycle riders have been the top contributor to road deaths for over a decade in Malaysia. With proper safety technology systems installed on the upcoming models, it is predicted that motorcycle crashes and deaths can be reduced in the next decade. This study aims to review the past, recent, and upcoming vehicle safety technologies from the motorcycle and passenger car perspective that can potentially help reduce motorcycle crashes and injury risks. Various safety technologies have been introduced for passenger cars such as anti-lock braking systems, electronic stability control, lane keep assistance, traction control, blind-spot detection, autonomous emergency braking, and many more. With the requirements of ASEAN NCAP, these technologies are introduced to make passenger cars safer and to avoid road collisions, especially with the motorcycle. However, the opposite is true for motorcycles where only expensive motorcycles have these safety technologies, while for lower- capacity motorcycles, only basic and compulsory technologies are introduced even though this group remains the top contributor to motorcycle crashes and road deaths in Malaysia. Therefore, it is suggested that more safety technologies be introduced for motorcycles especially lower-capacity models. While for passenger cars, there are newer technologies that can be introduced to improve the current offerings to enhance the safety of passenger cars and other vehicles on the road and reduce the risk of road fatalities.

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“…Advanced Driver Assistance Systems (ADAS) are enacted to protect road users in these circumstances. As a merger of numerous sophisticated systems, ADAS includes Adaptive Cruise Control (ACC), Autonomous Emergency Braking (AEB), Anti-lock Braking System (ABS), and many more [1,2]. Although ADAS can prevent a collision, pedestrianvehicle collisions are viewed as the most deliberate type of accident since they yield a high fatality rate.…”

Section: Introductionmentioning

confidence: 99%

Motion Planning and Tracking Trajectory of an Autonomous Emergency Braking Pedestrian (AEB-P) System Based on Different Brake Pad Friction Coefficients on Dry Road Surface

Zulkifli

Peeie²,

Zakaria³

et al. 2022

Int. J. Automot. Mech. Eng.

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Accidents between vehicles and pedestrians usually occur when a pedestrian is crossing the road. An Autonomous Emergency Braking Pedestrian (AEB-P) is introduced to prevent collisions between vehicles and pedestrians. However, the performance of an AEB-P will be reduced when the brake pad is worn out on a dry road. In this study, the motion planning, namely Vehicle Conditional Artificial Potential Field (VC-APF), including a warning signal and emergency brake phase that generate the vehicle’s deceleration, is proposed to analyze the effect of brake pad on the AEB-P performance. Then, the vehicle’s deceleration is tracked by the tracking trajectory, where the PI controller is adapted to provide the optimum braking force. The function of PI control is to ensure the vehicle’s deceleration is approaching the desired deceleration. The performance of the proposed method has been simulated on the dry road surface with different brake pad coefficients; 0.4, 0.35, and 0.24. The simulation results show that the vehicle manages to stop colliding with a pedestrian on the dry road surface at the minimum safety distance range of 2.7-2.9 meters.

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Autonomous Emergency Brake (AEB) for Pedestrian for ASEAN NCAP Safety Rating Consideration: A Review

Cited by 8 publications

References 9 publications

Vision-Based Autonomous Vehicle Systems Based on Deep Learning: A Systematic Literature Review

Vision-Based Autonomous Vehicle Systems Based on Deep Learning: A Systematic Literature Review

A Review of Motorcycle Safety Technologies from the Motorcycle and Passenger Car Perspectives

Motion Planning and Tracking Trajectory of an Autonomous Emergency Braking Pedestrian (AEB-P) System Based on Different Brake Pad Friction Coefficients on Dry Road Surface

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