Control system design for an Automatic Emergency Braking system in a sedan vehicle

Garcia-Bedoya, Olmer; Shogo, Hirota; Ferreira, Janito Vaqueiro

doi:10.1109/itslatam.2019.8721353

Cited by 7 publications

(3 citation statements)

References 9 publications

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“…Deep learning is used extensively in autonomous driving and augmented reality. Only a few of the complex and specialized functions needed for autonomous driving are automatic, for example, lane-keeping assistance, emergency braking (AEB) [1], active cruise control [2], forward collision warning (FCW) [3] and crash avoidance [4]. FCW and AEB are two of the first accident-avoidance features to be tested.…”

Section: Introductionmentioning

confidence: 99%

Navigating an Automated Driving Vehicle via the Early Fusion of Multi-Modality

Haris

Głowacz

2022

Sensors

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The ability of artificial intelligence to drive toward an intended destination is a key component of an autonomous vehicle. Different paradigms are now being employed to address artificial intelligence advancement. On the one hand, modular pipelines break down the driving model into submodels, such as perception, maneuver planning and control. On the other hand, we used the end-to-end driving method to assign raw sensor data directly to vehicle control signals. The latter is less well-studied but is becoming more popular since it is easier to use. This article focuses on end-to-end autonomous driving, using RGB pictures as the primary sensor input data. The autonomous vehicle is equipped with a camera and active sensors, such as LiDAR and Radar, for safe navigation. Active sensors (e.g., LiDAR) provide more accurate depth information than passive sensors. As a result, this paper examines whether combining the RGB from the camera and active depth information from LiDAR has better results in end-to-end artificial driving than using only a single modality. This paper focuses on the early fusion of multi-modality and demonstrates how it outperforms a single modality using the CARLA simulator.

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Section: Introductionmentioning

confidence: 99%

Navigating an Automated Driving Vehicle via the Early Fusion of Multi-Modality

Haris

Głowacz

2022

Sensors

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“…To solve the instability problem of the multiunit vehicle during the braking process, some researchers have investigated the brake model building and conducted stability analysis [1][2][3][4]. Some other researchers have put forward different approaches to improve the braking stability of the train [5,6].…”

Section: Introductionmentioning

confidence: 99%

Brake Instability Dynamic Model and Active Control Strategy for a Multiunit Articulated Rubber-Wheel Autonomous Rail Rapid Transit System

Zhang

Xiao

et al. 2022

Sustainability

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Due to the particularity of the structure, the dynamic properties of multiunit articulated rubber-wheel autonomous rail rapid transit system are very complex, which increases the difficulty of studying its braking stability. In this paper, a dynamic analysis model for the emergency braking of a multiunit articulated rubber-wheel autonomous rail rapid transit system is established by introducing the axle load transfer, suspension deformation compatibility equation, articulation force relationship equations, etc. Based on an in-depth analysis of the risks of the lateral swing instability and their formation mechanisms, an active control strategy for the multiunit articulated rubber-wheel autonomous rail rapid transit system under emergency braking conditions is innovatively proposed to ensure the stability of the vehicle, with the shortest braking distance as the optimization goal. Through simulation and experimentation, the established dynamic model is confirmed to approach the real vehicle well, and the feasibility of the active control strategy is proved.

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“…The AEBS is an autonomous road vehicle safety system which employs sensors to monitor the distance of vehicles in front. The microcontroller will detect situations where the relative speed and distance between the host and target vehicles to suggest if a collision is imminent [10]. In such situation, emergency braking can be automatically function to avoid collision or at least mitigate its effects.…”

Section: Introductionmentioning

confidence: 99%

Autonomous Vehicle Repositioning System for Double-Parking

Razif¹

2020

IJATCSE

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Double parking is commonly practiced in crowded areas where parking facilities are limited. Normally, the double-parked vehicle owner will leave their phone number on the vehicle or release the handbrake. However, there are number of cases that the owner forgot to take the action and causing problem for the blocked vehicle to leave the parking lot. This research will focus on solving the double parking issues by proposing an automatic approach for the vehicle to reposition itself and it is equipped with a notification system for the owner. The autonomous system started when a sound sensor detect a horn sound from the blocked vehicle. Then, the system will automatically start the engine and shift the gear position from neutral to reverse. At the same time, the brake system will functioning and it will released the brake about 5 seconds or 3.45 meters, when no obstacle behind the vehicle. The distance is set to 3.45 meters because it is enough for the blocked vehicle to leave the parking space. If there is any obstacle behind the vehicle, the autonomous system will not release the brake and the repositioning will be considered unsuccessful. After the repositioning process completed either success or not, the system will send a notification to the double-parked owner mobile phone through SMS immediately to alert the owner, regarding to their vehicles status. Based on the experiment, the autonomous vehicle repositioning system is able to move the blocked vehicle automatically to the new position as desired and success in sending notification to the owner regarding to their vehicle repositioning condition status.

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Control system design for an Automatic Emergency Braking system in a sedan vehicle

Cited by 7 publications

References 9 publications

Navigating an Automated Driving Vehicle via the Early Fusion of Multi-Modality

Navigating an Automated Driving Vehicle via the Early Fusion of Multi-Modality

Brake Instability Dynamic Model and Active Control Strategy for a Multiunit Articulated Rubber-Wheel Autonomous Rail Rapid Transit System

Autonomous Vehicle Repositioning System for Double-Parking

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