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

Alsuwian, Turki; Saeed, Rana Basharat; Amin, Arslan Ahmed

doi:10.3390/app12178458

Cited by 19 publications

(11 citation statements)

References 29 publications

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“…Besides planning and control, autonomous vehicles are required to drive safely. Aiming to this purpose, Alsuwian et al [ 40 ] presented an advanced emergency braking system (EBS) with multi-sensor fusion to brake the vehicle to avoid or mitigate a collision. Moreover, Diachuk et al [ 41 ] presented a safe and smooth motion planning technique that combined the finite element method (FEM) and nonlinear optimization for autonomous vehicles.…”

Section: Related Workmentioning

confidence: 99%

Development of an Autonomous Driving Vehicle for Garbage Collection in Residential Areas

Pyo

Bae

Joo

et al. 2022

Sensors

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Autonomous driving and its real-world implementation have been among the most actively studied topics in the past few years. In recent years, this growth has been accelerated by the development of advanced deep learning-based data processing technologies. Moreover, large automakers manufacture vehicles that can achieve partially or fully autonomous driving for driving on real roads. However, self-driving cars are limited to some areas with multi-lane roads, such as highways, and self-driving cars that drive in urban areas or residential complexes are still in the development stage. Among autonomous vehicles for various purposes, this paper focused on the development of autonomous vehicles for garbage collection in residential areas. Since we set the target environment of the vehicle as a residential complex, there is a difference from the target environment of a general autonomous vehicle. Therefore, in this paper, we defined ODD, including vehicle length, speed, and driving conditions for the development vehicle to drive in a residential area. In addition, to recognize the vehicle’s surroundings and respond to various situations, it is equipped with various sensors and additional devices that can notify the outside of the vehicle’s state or operate it in an emergency. In addition, an autonomous driving system capable of object recognition, lane recognition, route planning, vehicle manipulation, and abnormal situation detection was configured to suit the vehicle hardware and driving environment configured in this way. Finally, by performing autonomous driving in the actual experimental section with the developed vehicle, it was confirmed that the function of autonomous driving in the residential area works appropriately. Moreover, we confirmed that this vehicle would support garbage collection works through the experiment of work efficiency.

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Section: Related Workmentioning

confidence: 99%

Development of an Autonomous Driving Vehicle for Garbage Collection in Residential Areas

Pyo

Bae

Joo

et al. 2022

Sensors

View full text Add to dashboard Cite

show abstract

“…Vehicles equipped with autonomous driving technology will integrate into human life as a kind of robot [3]. In recent years, the automotive industry has made tremendous progress in autonomous driving technology, including lane-keeping [4], adaptive cruise control [5], and emergency braking systems [6,7]. As the operational environments of autonomous vehicles become increasingly diverse and complex, the focus has shifted from simple indoor environments to a wide range of outdoor environments.…”

Section: Introductionmentioning

confidence: 99%

Research on multitask model of object detection and road segmentation in unstructured road scenes

Gao,

Zhao,

Zhang

et al. 2024

Meas. Sci. Technol.

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With the rapid development of artificial intelligence and computer vision technology, autonomous driving technology has become a hot area of concern. The driving scenarios of autonomous vehicles can be divided into structured scenarios and unstructured scenarios.Compared with structured scenes, unstructured road scenes lack the constraints of lane lines and traffic rules, and the safety awareness of traffic participants is weaker. Therefore, there are new and higher requirements for the environment perception tasks of autonomous vehicles in unstructured road scenes. The current research rarely integrates the target detection and road segmentation to achieve the simultaneous processing of target detection and road segmentation of autonomous vehicle in unstructured road scenes. Aiming at the above issues, a multitask model for object detection and road segmentation in unstructured road scenes is proposed. Through the sharing and fusion of the object detection model and road segmentation model, multitask model can complete the tasks of multi-object detection and road segmentation in unstructured road scenes while inputting a picture. Firstly, MobileNetV2 is used to replace the backbone network of YOLOv5, and multi-scale feature fusion is used to realize the information exchange layer between different features.Subsequently, a road segmentation model was designed based on the DeepLabV3+ algorithm.Its main feature is that it uses MobileNetV2 as the backbone network and combines the binary classification focus Loss function for network optimization. Then, we fused the object detection algorithm and road segmentation algorithm based on the shared MobileNetV2 network to obtain a multitask model and trained it on both the public dataset and the self-built dataset NJFU. The training results demonstrate that the multitask model significantly enhances the algorithm's execution speed by approximately 10 FPS while maintaining the accuracy of object detection and road segmentation. Finally, we conducted validation of the multitask model on an actual vehicle.

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“…Fu et al [ 9 ] proposed a deep reinforcement learning (DRL)-based autonomous braking decision-making strategy in emergency situations by analyzing the vehicle lane changing process and braking process in detail, taking into account balance among the three key influencing factors of efficiency, accuracy, and passenger comfort to improve driving safety. Alsuwian [ 10 ] proposed an advanced sensor fusion based on an emergency braking system (EBS) that automatically identified possible collisions and activated the vehicle braking system. An emergency steering-collision-avoidance method based on safety distance as an index was proposed by Chen et al [ 11 ], which calculates the required safety distance encountering a stationary or abruptly decelerating vehicle at different speeds via slip angles and lateral force tire models.…”

Section: Introductionmentioning

confidence: 99%

A Hierarchical Prediction Method for Pedestrian Head Injury in Intelligent Vehicle with Combined Active and Passive Safety System

Shi,

Zhang,

et al. 2024

Biomimetics

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With the development of intelligent vehicle technology, the probability of road traffic accidents occurring has been effectively reduced to a certain extent. However, there is still insufficient research on head injuries in human vehicle collisions, making it impossible to effectively predict pedestrian head injuries in accidents. To study the efficacy of a combined active and passive safety system on pedestrian head protection through the combined effect of the exterior airbag and the braking control systems of an intelligent vehicle, a “vehicle–pedestrian” interaction system is constructed in this study and is verified by real collision cases. On this basis, a combined active and passive system database is developed to analyze the cross-influence of the engine hood airbag and the vehicle braking curve parameters on pedestrian HIC (head injury criterion). Meanwhile, a hierarchy design strategy for a combined active and passive system is proposed, and a rapid prediction of HIC is achieved via the establishment of a fitting equation for each grading. The results show that the exterior airbag can effectively protect the pedestrian’s head, prevent the collision between the pedestrian’s head and the vehicle front structure, and reduce the HIC. The braking parameter H2 is significantly correlated with head injury, and when H2 is less than 1.8, the HIC value is less than 1000 in nearly 90% of cases. The hierarchy design strategy and HIC prediction method of the combined active and passive system proposed in this paper can provide a theoretical basis for rapid selection and parameter design.

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

Cited by 19 publications

References 29 publications

Development of an Autonomous Driving Vehicle for Garbage Collection in Residential Areas

Development of an Autonomous Driving Vehicle for Garbage Collection in Residential Areas

Research on multitask model of object detection and road segmentation in unstructured road scenes

A Hierarchical Prediction Method for Pedestrian Head Injury in Intelligent Vehicle with Combined Active and Passive Safety System

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