Machine Learning Techniques in ADAS: A Review

Moujahid, Abdallah; Tantaoui, Mounir ElAraki; Hina, Manolo Dulva; Soukane, Assia; Ortalda, Andrea; ElKhadimi, Ahmed; Ramdane-Chérif, Amar

doi:10.1109/icacce.2018.8441758

Cited by 56 publications

(22 citation statements)

References 17 publications

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“…The existing techniques for forward collision warning include feature-based [ 25 ], stereo-based [ 4 , 26 ], motion-based [ 27 , 28 ], and learning-based [ 29 , 30 ] approaches. To detect the front vehicle, the underneath shadow is commonly adopted as a pattern from visual cues [ 31 ].…”

Section: Related Workmentioning

confidence: 99%

A Vision-Based Driver Assistance System with Forward Collision and Overtaking Detection

Lin

Dai

et al. 2020

Sensors

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One major concern in the development of intelligent vehicles is to improve the driving safety. It is also an essential issue for future autonomous driving and intelligent transportation. In this paper, we present a vision-based system for driving assistance. A front and a rear on-board camera are adopted for visual sensing and environment perception. The purpose is to avoid potential traffic accidents due to forward collision and vehicle overtaking, and assist the drivers or self-driving cars to perform safe lane change operations. The proposed techniques consist of lane change detection, forward collision warning, and overtaking vehicle identification. A new cumulative density function (CDF)-based symmetry verification method is proposed for the detection of front vehicles. The motion cue obtained from optical flow is used for overtaking detection. It is further combined with a convolutional neural network to remove repetitive patterns for more accurate overtaking vehicle identification. Our approach is able to adapt to a variety of highway and urban scenarios under different illumination conditions. The experiments and performance evaluation carried out on real scene images have demonstrated the effectiveness of the proposed techniques.

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Section: Related Workmentioning

confidence: 99%

A Vision-Based Driver Assistance System with Forward Collision and Overtaking Detection

Lin

Dai

et al. 2020

Sensors

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“…However, only one review has been found in the intersection of machine learning and ADAS systems. In [24], authors compare different methods and how the data pipeline is generally built when applying machine learning models to ADAS tasks.…”

Section: Related Workmentioning

confidence: 99%

Resource-Constrained Machine Learning for ADAS: A Systematic Review

Borrego-Carazo

Castells-Rufas

Biempica³

et al. 2020

IEEE Access

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The advent of machine learning (ML) methods for the industry has opened new possibilities in the automotive domain, especially for Advanced Driver Assistance Systems (ADAS). These methods mainly focus on specific problems ranging from traffic sign and light recognition to pedestrian detection. In most cases, the computational resources and power budget found in ADAS systems are constrained while most machine learning methods are computationally intensive. The usual solution consists in adapting the ML models to comply with the memory and real-time (RT) requirements for inference. Some models are easily adapted to resource-constrained hardware, such as Support Vector Machines, while others, like Neural Networks, need more complex processes to fit into the desired hardware. The ADAS hardware (HW platforms) are diverse, from complex MPSoC CPUs down to classical MCUs, DPSs and application-specific FPGAs and ASICs or specific GPU platforms (such as the NVIDIA families Tegra or Jetson). Therefore, there is a tradeoff between the complexity of the ML model implemented and the selected platform that impacts the performance metrics: function results, energy consumption and speed (latency and throughput). In this paper, a survey in the form of systematic review is conducted to analyze the scope of the published research works that embed ML models into resource-constrained implementations for ADAS applications and what are the achievements regarding the ML performance, energy and speed trade-off. INDEX TERMS Machine learning, embedded software, automotive engineering, GPU, FPGA, ADAS. I. INTRODUCTION Safety is increasingly important for drivers and other users of the driving environment such as pedestrians, cyclists, bikers, scooters, etc. Only in 2016, road accidents in the European Union summed up to 25.600 fatalities and 1.4 million people injured [1]. Many measures are being taken by public institutions (improving road quality, enhancing driver consciousness and driving regulations, etc.) to improve these numbers. However, the cause of most of these accidents resides in human errors or distractions. For this reason, automotive firms are pushing up research on improving safety. The cornerstone of these advances are Advanced Driver Assistance Systems (ADAS). ADAS systems consist in several sensors, processing and actuators components that help drivers to avoid accidents and The associate editor coordinating the review of this manuscript and approving it for publication was Ye Duan.

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“…AI utilization in ADAS has been a very active field of research in the last decade [86]. Roughly speaking, these are two main application lines that can be currently identified.…”

Section: Ai Components In Adasmentioning

confidence: 99%

On Driver Behavior Recognition for Increased Safety: A Roadmap

Davoli

Martalò

Cilfone

et al. 2020

Safety

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Advanced Driver-Assistance Systems (ADASs) are used for increasing safety in the automotive domain, yet current ADASs notably operate without taking into account drivers’ states, e.g., whether she/he is emotionally apt to drive. In this paper, we first review the state-of-the-art of emotional and cognitive analysis for ADAS: we consider psychological models, the sensors needed for capturing physiological signals, and the typical algorithms used for human emotion classification. Our investigation highlights a lack of advanced Driver Monitoring Systems (DMSs) for ADASs, which could increase driving quality and security for both drivers and passengers. We then provide our view on a novel perception architecture for driver monitoring, built around the concept of Driver Complex State (DCS). DCS relies on multiple non-obtrusive sensors and Artificial Intelligence (AI) for uncovering the driver state and uses it to implement innovative Human–Machine Interface (HMI) functionalities. This concept will be implemented and validated in the recently EU-funded NextPerception project, which is briefly introduced.

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Machine Learning Techniques in ADAS: A Review

Cited by 56 publications

References 17 publications

A Vision-Based Driver Assistance System with Forward Collision and Overtaking Detection

A Vision-Based Driver Assistance System with Forward Collision and Overtaking Detection

Resource-Constrained Machine Learning for ADAS: A Systematic Review

On Driver Behavior Recognition for Increased Safety: A Roadmap

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