Reasoning-Based Framework for Driving Safety Monitoring Using Driving Event Recognition

Wu, Bing-Fei; Chen, Ying-Han; Yeh, Chung-Hsuan; Li, Yen-Feng

doi:10.1109/tits.2013.2257759

Cited by 33 publications

(17 citation statements)

References 38 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The other approach to detect the driver state, is to retrieve data from car behavior, such as distance to the front car or the lateral acceleration, and use it to monitor the driving behavior and infer a danger degree [72]. The problem to infer the dangerousness of a driving style or behavior is the lack of a standardized set of rules to define it.…”

Section: B Driver's State Behavior and Identificationmentioning

confidence: 99%

See 1 more Smart Citation

Resource-Constrained Machine Learning for ADAS: A Systematic Review

Borrego-Carazo

Castells-Rufas

Biempica³

et al. 2020

IEEE Access

View full text Add to dashboard Cite

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.

show abstract

Section: B Driver's State Behavior and Identificationmentioning

confidence: 99%

“…Only one example of driver behavior determination through car driving signals [72] has been found in the review process. Authors record data such as distance to next car or frontal/lateral accelerations to estimate the driving behavior: accelerating, decelerating, moving left or right and more.…”

Section: ) Algorithms Structurementioning

confidence: 99%

Resource-Constrained Machine Learning for ADAS: A Systematic Review

Borrego-Carazo

Castells-Rufas

Biempica³

et al. 2020

IEEE Access

View full text Add to dashboard Cite

show abstract

“…Among the papers that used signal preprocessing, about 60% of them used statistical techniques. In the frequency domain, the filters infinite impulse response [72] and Butterworth [28,49,52,64,71] were used in the removal of signal components. Already the Fast Fourier Transform (FFT) [3,17,18] and Wavelets [10,19,20,25,62,69] were used in the noise removal and feature extraction.…”

Section: Data Preprocessing Stepmentioning

confidence: 99%

“…Regarding proprioception, Wu et al [71] recognized driving events as normal driving, acceleration, deceleration, changing to the left or right lanes, zigzag driving, and approaching the car in front through a Hidden Markov models. The danger-level indicator was established by a Fuzzy Logic system.…”

Section: Other Approachesmentioning

confidence: 99%

See 1 more Smart Citation

Vehicular Perception Based on Inertial Sensing: A Structured Mapping of Approaches and Methods

Menegazzo

Wangenheim

2020

SN COMPUT. SCI.

View full text Add to dashboard Cite

In this paper, we present a structured literature mapping of the state-of-the-art of vehicular perception methods and approaches using inertial sensors. An in-depth investigation and classification were performed employing the results of a systematic literature review. The analysis focused on identifying methods that capture signals provided by inertial sensors such as accelerometers and gyroscopes to recognize transient or persistent events associated with the vehicle's movement. We classified these events into vehicular exteroception, associated with potholes, cracks, speed bumps, pavement type, conservation state; and vehicular proprioception, associated with lane change, braking, skidding, aquaplaning, turning right or left. Through the comprehensive study of publications in a 7-year time window, in addition to the methods, we have also identified their dependency factors, hardware platforms and applications.

show abstract