Development of Sensor Fusion Based ADAS Modules in Virtual Environments

Ilic, Velibor; Marijan, Mališa; Mehmed, Ayhan; Antlanger, Moritz

doi:10.1109/zinc.2018.8448849

Cited by 10 publications

(6 citation statements)

References 7 publications

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“…Mono-sensor systems, on the other hand, are very light computationally; however, they also provide very poor accuracy. If an ADAS algorithm needs to be run on a less-expensive embedded platform, which has lesser hardware resources (a smaller number of CPU and GPU cores and less cache and RAM), like a Cortex-M-based STM32 platform [ 55 , 56 ], and less accuracy of EBA is acceptable, OCSF shall prove to be a comparatively better option. However, if hardware resources and computational cost are not a concern, and the accuracy and precision of the ADAS algorithm utilising the fusion architecture is of the utmost importance, ODSF is a more favourable option for driving EBA [ 57 , 58 , 59 , 60 ].…”

Section: Discussionmentioning

confidence: 99%

Centralised and Decentralised Sensor Fusion-Based Emergency Brake Assist

Deo¹,

Palade

Huda

2021

Sensors

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Many advanced driver assistance systems (ADAS) are currently trying to utilise multi-sensor architectures, where the driver assistance algorithm receives data from a multitude of sensors. As mono-sensor systems cannot provide reliable and consistent readings under all circumstances because of errors and other limitations, fusing data from multiple sensors ensures that the environmental parameters are perceived correctly and reliably for most scenarios, thereby substantially improving the reliability of the multi-sensor-based automotive systems. This paper first highlights the significance of efficiently fusing data from multiple sensors in ADAS features. An emergency brake assist (EBA) system is showcased using multiple sensors, namely, a light detection and ranging (LiDAR) sensor and camera. The architectures of the proposed ‘centralised’ and ‘decentralised’ sensor fusion approaches for EBA are discussed along with their constituents, i.e., the detection algorithms, the fusion algorithm, and the tracking algorithm. The centralised and decentralised architectures are built and analytically compared, and the performance of these two fusion architectures for EBA are evaluated in terms of speed of execution, accuracy, and computational cost. While both fusion methods are seen to drive the EBA application at an acceptable frame rate (~20fps or higher) on an Intel i5-based Ubuntu system, it was concluded through the experiments and analytical comparisons that the decentralised fusion-driven EBA leads to higher accuracy; however, it has the downside of a higher computational cost. The centralised fusion-driven EBA yields comparatively less accurate results, but with the benefits of a higher frame rate and lesser computational cost.

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Section: Discussionmentioning

confidence: 99%

Centralised and Decentralised Sensor Fusion-Based Emergency Brake Assist

Deo¹,

Palade

Huda

2021

Sensors

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“…Combined KF and adaptive neuro-fuzzy inference system (ANFIS) in reference [113], an effective information combination method constructed for the target tracking framework, which has better precision and performance than the traditional KF algorithm. Similarly, in reference [37], according to the weighted average of the predicted states and the estimated status update based on the current measurement, the lower weights are given to the states with higher uncertainties in the measurement update step. Besides, FKF is applied to process the measurements from radar, LiDAR, camera, and other sensors, reducing the statistical noise and other errors.…”

Section: ) the Methods Based On Kalman Filtermentioning

confidence: 99%

“…Some studies utilized open-source data set [35], [36] or generated them from simulation software [37] to avoid the laborious collection of sensor data. The study of multi-sensor fusion requires a large amount of data, especially in the context of a large number of applications for deep learning.…”

Section: Introductionmentioning

confidence: 99%

Multi-Sensor Fusion in Automated Driving: A Survey

2020

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With the significant development of practicability in deep learning and the ultra-highspeed information transmission rate of 5G communication technology will overcome the barrier of data transmission on the Internet of Vehicles, automated driving is becoming a pivotal technology affecting the future industry. Sensors are the key to the perception of the outside world in the automated driving system and whose cooperation performance directly determines the safety of automated driving vehicles. In this survey, we mainly discuss the different strategies of multi-sensor fusion in automated driving in recent years. The performance of conventional sensors and the necessity of multi-sensor fusion are analyzed, including radar, LiDAR, camera, ultrasonic, GPS, IMU, and V2X. According to the differences in the latest studies, we divide the fusion strategies into four categories and point out some shortcomings. Sensor fusion is mainly applied for multi-target tracking and environment reconstruction. We discuss the method of establishing a motion model and data association in multi-target tracking. At the end of the paper, we analyzed the deficiencies in the current studies and put forward some suggestions for further improvement in the future. Through this investigation, we hope to analyze the current situation of multi-sensor fusion in the automated driving process and provide more efficient and reliable fusion strategies.

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“…This functionality enhances both driving comfort and safety, particularly in extended driving scenarios. The enhanced algorithm proposed in this paper is validated by analysing its performance in a virtual simulation environment [1,2] using a co-simulation framework by Automated Driving Toolbox -MATLAB, which facilitates the modelling of Algorithms in Simulink and performance visualisation in a simulated environment rendered through Unreal Engine by Epic Games.…”

Section: Introductionmentioning

confidence: 99%

Modelling, Simulation and Virtual validation of Adaptive Cruise Control (ACC) Algorithm based on Sensor Fusion

2024

Proceedings of the First International Conference on Science, Engineering and Technology Practices for Sustainable Development,

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Adaptive Cruise Control (ACC) systems are rapidly becoming an integral part of modern vehicles, contributing to both enhanced driving comfort and increased road safety. Traditional approaches for ACC often rely on a single type of sensor, which could be limiting in dynamically evolving driving scenarios. To improve the system's performance in such complex driving conditions, an enhanced approach using Sensor fusion technique has been discussed. Further, based on the detections the Most Important Object (MIO) lead car is obtained, with which a robust stateful control algorithm determines the appropriate set speed for the Ego vehicle to maintain safe distance with the preceding (lead car) in the same lane, based on the states of Ego and Lead vehicle. This set speed of the Ego Vehicle is maintained by a Vehicle Longitudinal controller. The modelled system has been validated in a closed-loop Virtual Environment with custom scenarios for intricate analysis of system performance.

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Development of Sensor Fusion Based ADAS Modules in Virtual Environments

Cited by 10 publications

References 7 publications

Centralised and Decentralised Sensor Fusion-Based Emergency Brake Assist

Centralised and Decentralised Sensor Fusion-Based Emergency Brake Assist

Multi-Sensor Fusion in Automated Driving: A Survey

Modelling, Simulation and Virtual validation of Adaptive Cruise Control (ACC) Algorithm based on Sensor Fusion

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