Environmental Perception for Intelligent Vehicles

Armingol, José María; Alfonso, Jorge; Aliane, Nourdine; Clavijo, Miguel; Campos-Cordobés, Sergio; Escalera, Arturo de la; Ser, Javier Del; Andrés, Javier Fernández; García, Fernando; López, Antonio; Mata, M. E. R. M. C.; Martín, David; Menéndez, José Manuel; Sánchez-Cubillo, Javier; Vázquez, David; Villalonga, Gabriel

doi:10.1016/b978-0-12-812800-8.00002-3

Cited by 11 publications

(9 citation statements)

References 113 publications

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“…Ultrasonic sensors work on the principle of emitting sound waves and listening to its reflection back from an object to calculate the distance between the object and the sensor [80], [81]. The sound waves are generated at a frequency too high, i.e., 25-50 KHz, for the human hearing frequency band [82]. The basic principle used to calculate the distance is similar to the one used by radars or LiDARs, i.e., emit a wave, wait until the bounced off wave from an object arrives, and calculate the distance based on how long it took for the wave to reflect back by using this simple formula:…”

Section: ) Sonarmentioning

confidence: 99%

Unmanned Aerial Vehicles (UAVs): Collision Avoidance Systems and Approaches

et al. 2020

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Moving towards autonomy, unmanned vehicles rely heavily on state-of-the-art collision avoidance systems (CAS). A lot of work is being done to make the CAS as safe and reliable as possible, necessitating a comparative study of the recent work in this important area. The paper provides a comprehensive review of collision avoidance strategies used for unmanned vehicles, with the main emphasis on unmanned aerial vehicles (UAV). It is an in-depth survey of different collision avoidance techniques that are categorically explained along with a comparative analysis of the considered approaches w.r.t. different scenarios and technical aspects. This also includes a discussion on the use of different types of sensors for collision avoidance in the context of UAVs. INDEX TERMS autonomous aerial vehicles, autonomous vehicles, collision avoidance, active and passive sensors, optimisation-based, force-field based, sense and avoid, geometry based

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Section: ) Sonarmentioning

confidence: 99%

Unmanned Aerial Vehicles (UAVs): Collision Avoidance Systems and Approaches

et al. 2020

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“…An acoustic array sensor detects sound or vibration of specific frequencies to determine the distance and direction of the sound source that created the sound or the reflector that reflected the sound. This type of localization technique is known as the passive acoustic location technique [74,100]. In SPS, Acoustic Array Sensor is used to detect parking lot vacancy and for surveillance purposes.…”

Section: Acoustic Array Sensormentioning

confidence: 99%

“…Ultrasonic sensor uses acoustic waves in the range of 25 kHz to 50kHz to detect any nearby object that reflects the acoustic wave [100]. This sensor is best suited for indoor applications due to not having the susceptibility to work under environmental changes such as snow and rain.…”

Section: Ultrasonic Sensormentioning

confidence: 99%

Smart parking systems: comprehensive review based on various aspects

Fahim

Hasan

Chowdhury

2021

Heliyon

123

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Parking allocation has become a major problem in modern cities for which numerous smart parking systems (SPS) have been developed. This paper aims to provide comprehensive study, comparison and extensive analysis of SPSs in terms of technological approach, sensors utilized, networking technologies, user interface, computational approaches, and service provided. Moreover, the paper fills up the research gap by providing a clear insight into the suitability of SPSs in various environmental conditions and highlights their advantages/disadvantages. The extensive comparison among multiple aspects of SPSs would enable researchers, designers, and policymakers to identify the best suited SPS and understand the current trends in this sector.

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“…[5][6][7] For example, forwardlooking sensors, such as radar, visible-light cameras (VLCs), and light detection and ranging (LiDAR), are commonly applied in the detection of surrounding objects. [8,9] Although VLCs record information regarding surrounding vehicles and pedestrians, [10] they are unreliable in low-illumination environments and adverse weather conditions, thereby reducing the detection accuracy. [11,12] In addition, although LiDAR can detect surrounding objects under darkness using the scanning laser, [9] it is susceptible to weather conditions.…”

Section: Introductionmentioning

confidence: 99%

MXene‐Composite‐Enabled Ultra‐long‐Distance Detection and Highly Sensitive Self‐Powered Noncontact Triboelectric Sensors and Their Applications in Intelligent Vehicle Perception

Zhao,

Wang,

Wang

et al. 2023

Adv Funct Materials

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With the ongoing advancement in sensor technologies, the development of intelligent, secure, and low‐carbon vehicles will be possible in the near future. However, providing continuous power to numerous onboard sensors consumes substantial amounts of power from vehicle, resulting in the shortened running times. Herein, a self‐powered noncontact triboelectric nanogenerator (SNC‐TENG) with ultralong‐distance detection and a high sensitivity is proposed, which can be used as ambient perception sensors for smart vehicles. The SNC‐TENG is constructed from a MXene/silicone nanocomposite with a fully embedded conductive sponge, which significantly enriches the tribo‐charges and leads to an excellent capability and performance. This SNC‐TENG is capable of perceiving human activity from a distance of 2 m. To the best of authors knowledge, the perceiving distance per unit area of this SNC‐TENG is the highest among previously reported non‐contact TENGs. Furthermore, the applicability of the SNC‐TENG is explored for vehicle sentry mode and blind spot detection for the first time. The use of this technology in a real vehicle demonstrates its feasibility for use in practical applications. Overall, this study provides a new design scheme for contactless self‐powered sensors, and the presented SNC‐TENG is expected to be applicable in various fields where non‐contact sensing is required.

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Environmental Perception for Intelligent Vehicles

Cited by 11 publications

References 113 publications

Unmanned Aerial Vehicles (UAVs): Collision Avoidance Systems and Approaches

Unmanned Aerial Vehicles (UAVs): Collision Avoidance Systems and Approaches

Smart parking systems: comprehensive review based on various aspects

MXene‐Composite‐Enabled Ultra‐long‐Distance Detection and Highly Sensitive Self‐Powered Noncontact Triboelectric Sensors and Their Applications in Intelligent Vehicle Perception

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