The effectiveness of the AEB system in the context of the safety of vulnerable road users

Ucińska, M.; Pełka, Małgorzata

doi:10.1515/eng-2021-0097

Cited by 4 publications

(4 citation statements)

References 9 publications

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“…If the system concludes that the vehicle's speed is greater than the speed of the identified item in front of the car, Emergency Braking Light Applied Braking Figure 1. Overview of the AEB process [3].…”

Section: Emergency Braking Systemmentioning

confidence: 99%

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Autonomous Vehicle with Emergency Braking Algorithm Based on Multi-Sensor Fusion and Super Twisting Speed Controller

2022

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The automobile revolution and growth in the number of cars produced several issues, and vehicle accidents remain one of the most serious road-related issues. Human mistakes and a failure to brake quickly are the main causes of accidents. There may be serious outcomes to driving when distracted. To address the aforementioned issues, an autonomous emergency braking system (AEBS) was developed. To support such an AEBS, scalable, reliable, secure, fault-tolerant, and interoperable technologies are required. An advanced emergency braking system (EBS) with sensor fusion is proposed in this paper that can autonomously identify a probable forward collision and activate the vehicle braking system to brake the vehicle to avoid or mitigate a collision. Additionally, it provides a non-linear speed controller that facilitates the AEBS to apply the brakes in an emergency. Sensor fusion using lidar, radar, and vision sensors makes the AEBS more efficient and more reliable to detect vehicles or obstacles and decreases the chance of collision to a minimum level. A MATLAB/Simulink environment was used for simulation experiments and the results demonstrated the stable operation of the AEBS to avoid forward collisions in the event of an error in the measurement of any one sensor while any vehicle is detected. The presented work establishes that the EBS sensor fusion unit is a highly reliable solution for detecting the leading vehicle at the proper time and the AEBS controller can apply the brake in the situation of forwarding obstacle detection.

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Section: Emergency Braking Systemmentioning

confidence: 99%

“…The two stages of partial braking (PB) and full braking (FB) make up the cascaded braking. Equation (3) in StoppingTimeCalculation is used to determine the stopping period for the FCW, and both the initial and second phases of PB and FB are calculated in the manner described below:…”

Section: Aeb Controllermentioning

confidence: 99%

Autonomous Vehicle with Emergency Braking Algorithm Based on Multi-Sensor Fusion and Super Twisting Speed Controller

2022

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“…At the end of 2020, about 16,696 VRUs died from traffic accidents, accounting for 27.1% of the deaths that year. This number will show an upward trend with the continuous increase in the population and the complexity of traffic scenarios; therefore, the safety of VRUs has received an increasing amount of attention in recent years [ 1 , 2 , 3 ].…”

Section: Introductionmentioning

confidence: 99%

An Improved YOLOv5 Algorithm for Vulnerable Road User Detection

Yang,

Tang,

Jiang

et al. 2023

Sensors

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The vulnerable road users (VRUs), being small and exhibiting random movements, increase the difficulty of object detection of the autonomous emergency braking system for vulnerable road users AEBS-VRUs, with their behaviors highly random. To overcome existing problems of AEBS-VRU object detection, an enhanced YOLOv5 algorithm is proposed. While the Complete Intersection over Union-Loss (CIoU-Loss) and Distance Intersection over Union-Non-Maximum Suppression (DIoU-NMS) are fused to improve the model’s convergent speed, the algorithm also incorporates a minor object detection layer to increase the performance of VRU detection. A dataset for complex AEBS-VRUS scenarios is established based on existing datasets such as Caltech, nuScenes, and Penn-Fudan, and the model is trained using migration learning based on the PyTorch framework. A number of comparative experiments using models such as YOLOv6, YOLOv7, YOLOv8 and YOLOx are carried out. The results of the comparative evaluation show that the proposed improved YOLO5 algorithm has the best overall performance in terms of efficiency, accuracy and timeliness of target detection.

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“…Note that in challenging environments, such as urban and dense areas, tunnels, etc., the localization error of modern GPS sensors is orders of magnitude higher than this level [8]. Moreover, an increasing factor to the difficulty of the problem is the fact that the prediction window of active perception systems is very short, since the AV should be able to timely adapt to abrupt changes in the surrounding environment (in a fraction of a second [9]), such as the "random" motion style of vulnerable users including pedestrians and cyclists. Additionally, the complexity of the surrounding environment requires the use of multiple sensing modalities (including cameras and LiDAR) for increased effectiveness [10].…”

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confidence: 99%

Accelerating Deep Neural Networks for Efficient Scene Understanding in Multi-Modal Automotive Applications

et al. 2023

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Environment perception constitutes one of the most critical operations performed by semiand fully-autonomous vehicles. In recent years, Deep Neural Networks (DNNs) have become the standard tool for perception solutions owing to their impressive capabilities in analyzing and modelling complex and dynamic scenes, from (often muti-modal) sensory inputs. However, the well-established performance of DNNs comes at the cost of increased time and storage complexity, which may become problematic in automotive perception systems due to the requirement for a short prediction horizon (as in many cases inference must be performed in real time) and the limited computational, storage, and energy resources of mobile systems. A common way of addressing this problem is to transform the original large pretrained networks into new smaller models, by utilizing Model Compression and Acceleration (MCA) techniques, improving both their storage and execution efficiency. Within the MCA framework, in this paper, we investigate the application of two state-of-the-art weight-sharing MCA techniques, namely a Vector Quantization (VQ) and a Dictionary Learning (DL) one, as well as two novel extensions, towards the acceleration and compression of widely used DNNs for 2D and 3D object-detection in automotive applications. Apart from the individual (uni-modal) networks, we also present and evaluate a multi-modal late-fusion algorithm for combining the detection results of the 2D and 3D detectors. Our evaluation studies are carried out on the KITTI Dataset. The obtained results lend themselves to a twofold interpretation. On the one hand, they showcase the significant acceleration and compression gains that can be achieved via the application of weight sharing on the selected DNN detectors, with limited accuracy loss, as well as highlight the performance differences between the two utilized weight sharing approaches. On the other, they demonstrate the substantial boost in detection performance obtained by combining the outcome of the two unimodal individual detectors, using the proposed late-fusion based multi-modal approach. Indeed, as our experiments have shown, pairing the high-performance DL-based MCA technique with the loss-mitigating effect of the multi-modal fusion approach, leads to highly accelerated models (up to approximately 2.5× and 6× for the 2D and 3D detectors, respectively) with the performance loss of the fused results ranging in most cases within single-digits figures (as low as around 1% for the class "cars").INDEX TERMS model compression and acceleration, multi-modal fusion, object detection, scene analysis, scene understanding, experimental evaluation I. INTRODUCTIONAutonomous vehicles (AV) are an integral part of the continuously evolving field of Intelligent Transportation Systems (ITS) [1], and introduce a variety of technical challenges intertwined with the levels of driving automation, as defined for example by the Society of Automobile Engineers (SAE) J3016 standard [2], ranging from "no driving automation" (level 0) to ...

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The effectiveness of the AEB system in the context of the safety of vulnerable road users

Cited by 4 publications

References 9 publications

Autonomous Vehicle with Emergency Braking Algorithm Based on Multi-Sensor Fusion and Super Twisting Speed Controller

Autonomous Vehicle with Emergency Braking Algorithm Based on Multi-Sensor Fusion and Super Twisting Speed Controller

An Improved YOLOv5 Algorithm for Vulnerable Road User Detection

Accelerating Deep Neural Networks for Efficient Scene Understanding in Multi-Modal Automotive Applications

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