Review of Advanced Driver Assistance Systems and Their Applications for Collision Avoidance in Urban Driving Scenario

Narkhede, Manish; Chopade, Nilkanth B.

doi:10.1007/978-3-030-82469-3_23

Cited by 6 publications

(4 citation statements)

References 37 publications

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“…It is also worth noting that, because there is no standardized distorted visual message for road mirrors, the image transmitted by the mirror may not be clear and understandable in the short term. This can result in erroneous actions being taken and resulting in a collision or accident [16] . So this means that each road mirror has its own individual characteristics.…”

Section: Discussionmentioning

confidence: 99%

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Analysis of the suitability of road mirrors for future telematics solutions

Masłowski

2023

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<p>A huge and quite important role in safe exit from the street with limited visibility plays traffic mirrors. Traffic mirrors, also known as safety mirrors or blind spot mirrors, have a significant impact on road safety. These mirrors are designed with a curved surface that allows for a wider field of view, making it easier for drivers to see objects and vehicles that may be in their blind spots. In order to commit a safe maneuver driver should be confident that the car moving down the street is at a sufficient and safe distance from the exit. Of course, the driver needs to have a perception of the time of the moving car to the place of exit from the street. All of these things play a significant role in safety exit and avoiding collisions. Traffic mirrors ensure an overview of the situation on the road. Moreover, the driver can measure the approximate distance by counting time for approaching the car. The experiment presented in the work is based on the time of the car’s approach. The purpose of this article is to detect the optimal time as well as distance before the street with limited visibility in order to exit the street safely and to avoid collisions. In this paper, we provide systematic approach to the research of a data and statistics of this data.</p>

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Section: Discussionmentioning

confidence: 99%

“…The driver receives more than 90% of the safety-relevant information necessary for driving with his or her eyes. Attention to an uninterrupted visual message is an essential element of the active safety system for road users in the operation of transport infrastructure [16] .…”

Section: Discussionmentioning

confidence: 99%

Analysis of the suitability of road mirrors for future telematics solutions

Masłowski

2023

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“…Teknologi pengenalan rambu lalu lintas yang sering disebut TSR, digunakan untuk mengidentifikasi rambu lalu lintas melalui penggunaan pengolahan citra. TSR sendiri telah teraplikasi pada sistem pembantu pengemudi (DAS) [2], [3], sistem pembantu pengemudi tingkat lanjut (ADAS) [4]- [6], sistem mengemudi otonom (ADS) [7], [8], yang disebut terakhir saat ini sudah mulai dilirik oleh banyak konsumen contohnya mobil listrik yang juga mobil pintar Tesla. TSR juga dapat diimplementasikan untuk keamanan jalan raya, pemahaman suasana perkotaan, dan peninjauan rambu lalu lintas untuk kebutuhan perawatan.…”

Section: Pendahuluanunclassified

Multi-Scale Convolutional Networks untuk Pengenalan Rambu Lalu Lintas di Indonesia

Akbar¹,

Purnomo²,

Supatman³

2022

SISFOKOM

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Teknologi pengenalan rambu lalu lintas yang sering disebut dengan traffic sign recognition (TSR), digunakan untuk mengenali rambu lalu lintas melalui pemanfaatan pengolahan citra. TSR sendiri dapat diaplikasikan pada sistem pembantu pengemudi, sistem pembantu pengemudi tingkat lanjut, sistem mengemudi otonom, keamanan jalan raya, pemahaman suasana perkotaan, dan pemantauan rambu untuk kepentingan perawatan. Perbaruan dari pengenalan rambu lalu lintas di Indonesia menggunakan multi-scale convolutional neural network (CNN) telah disajikan pada artikel ini. Dataset yang digunakan pada penelitian ini berjumlah 2050 data citra rambu lalu lintas yang dikelompokkan kedalam 10 kelas. Model CNN terdiri dari tiga lapisan konvolusi berukuran 3x3, tiga lapisan penggabungan (Maxpool) berukuran 2x2 dan satu lapisan fully-connected yang memanfaatkan fungsi aktivasi Softmax. Jumlah filter yang digunakan pada setiap lapisan konvolusi adalah 32. Algoritma pelatihan yang digunakan yaitu Stochastic gradient descent (SGD). Dengan menggunakan 1750 data citra latih, nilai epoch 20, dan laju pelatihan 0,005, nilai galat dan nilai akurasi yang didapatkan pada tahap pelatihan adalah masing-masing 0,0026 dan 100%. Sedangkan pada tahap pengujian, dengan 300 data citra uji, model CNN mampu memperoleh nilai galat 0,017 dan nilai akurasi mencapai 99,67%.

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“…With the continuous advancement of smart vehicles, advanced driving assistance systems (ADASs) are being utilized more frequently [ 1 , 2 , 3 ]. Among its many functions, the Adaptive Cruise Control (ACC) system stands out due to its ability to manage the vehicle’s longitudinal speed and maintain a safe distance from the vehicle in front.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Cruise System Based on Fuzzy MPC and Machine Learning State Observer

Guo

Wang

Liang

et al. 2023

Sensors

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Under the trend of vehicle intelligentization, many electrical control functions and control methods have been proposed to improve vehicle comfort and safety, among which the Adaptive Cruise Control (ACC) system is a typical example. However, the tracking performance, comfort and control robustness of the ACC system need more attention under uncertain environments and changing motion states. Therefore, this paper proposes a hierarchical control strategy, including a dynamic normal wheel load observer, a Fuzzy Model Predictive Controller and an integral-separate PID executive layer controller. Firstly, a deep learning-based dynamic normal wheel load observer is added to the perception layer of the conventional ACC system and the observer output is used as a prerequisite for brake torque allocation. Secondly, a Fuzzy Model Predictive Control (fuzzy-MPC) method is adopted in the ACC system controller design, which establishes performance indicators, including tracking performance and comfort, as objective functions, dynamically adjusts their weights and determines constraint conditions based on safety indicators to adapt to continuously changing driving scenarios. Finally, the executive controller adopts the integral-separate PID method to follow the vehicle’s longitudinal motion commands, thus improving the system’s response speed and execution accuracy. A rule-based ABS control method was also developed to further improve the driving safety of vehicles under different road conditions. The proposed strategy has been simulated and validated in different typical driving scenarios and the results show that the proposed method provides better tracking accuracy and stability than traditional techniques.

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Review of Advanced Driver Assistance Systems and Their Applications for Collision Avoidance in Urban Driving Scenario

Cited by 6 publications

References 37 publications

Analysis of the suitability of road mirrors for future telematics solutions

Analysis of the suitability of road mirrors for future telematics solutions

Multi-Scale Convolutional Networks untuk Pengenalan Rambu Lalu Lintas di Indonesia

Adaptive Cruise System Based on Fuzzy MPC and Machine Learning State Observer

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