A Novel Lane Departure Warning System for Improving Road Safety

Chen, Yue; Boukerche, Azzedine

doi:10.1109/icc40277.2020.9149085

Cited by 10 publications

(9 citation statements)

References 17 publications

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“…In order to gain a greater understanding of the applications and functionality of existing state-of-the-art sensors, this paper reviews ADAS currently available on the market. The following subsections review ten control systems of ADAS for smart EV: ACC [8], AEBS [9], LDW [10], [11], LKA [13], LCA [12], Night Vision [14], TSR [15], Pedestrian Detection [16], Automatic Parking [17], Traction control [18].…”

Section: A Adasmentioning

confidence: 99%

“…The LDW system [11] provides warning for drivers when the vehicle unintentionally leaves its present lane. During the process, the system follows lane markings with forwardlooking vision systems, defining the area within the current route, providing appropriate warnings [10].…”

Section: ) Advanced Emergency Braking System (Aebs)mentioning

confidence: 99%

“…[36] provides a detailed description of some of the vision-based lane detection and departure warning systems and highlighted the problem of lane detection under different complex environmental conditions. [11] presents an improvised novel LDWS model for image processing, lane detection, and lane departure recognition. The algorithm retains necessary portions of the road lane and removes useless details during the image processing stage, which minimizes the possibility of false warnings caused by false lane detection.…”

Section: ) Advanced Emergency Braking System (Aebs)mentioning

confidence: 99%

“…Figure 1 shows Driving Control Systems (DCS) and algorithms for a smart EV. DCS encompasses control methods and control modes based on ADAS [7], including Adaptive Cruise Control (ACC) [8], Automatic Emergency Braking System (AEBS) [9], Lane Departure Warning (LDW) [10], [11], Lane Change Assist (LCA) [12], Lane Keeping Assist (LKA) [13], Night Vision [14], Traffic Sign Recognition (TSR) [15], Pedestrian Detection [16], Automatic Parking [17], and Traction control [18]. Traffic flow parameters, driver behavior, and driving conditions can be detected and shared with vehicles within their vicinity.…”

Section: Introductionmentioning

confidence: 99%

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A Review on Driving Control Issues for Smart Electric Vehicles

Haque

Rahman²,

Islam

et al. 2021

IEEE Access

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Smart electric vehicles (EVs) are attractive because of their clean, zero-emission, low impact on the environment whilst providing a safer and smoother riding experience. To provide the latter, driving control requires appropriate systems and algorithms to optimize smart vehicle performance, maximize vehicle stability and protection, minimize accident probability, heighten driving comfort, and optimize transportation costs. Despite advancements in these areas, the realization of optimal smart EVs still requires considerable effort. This paper reviews driving control systems and algorithms for smart EVs, including the advanced driving assistant system, implementation of sensors, vehicle dynamics, and control algorithms. The major contribution of this review is to identify promising work to assist researchers with the most advanced trends in this area for prospective regulations. INDEX TERMSSmart Electric Vehicles (EVs), Driving Control Systems (DCS), Advanced Driving Assistance System (ADAS), Algorithms.

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Section: A Adasmentioning

confidence: 99%

Section: ) Advanced Emergency Braking System (Aebs)mentioning

confidence: 99%

Section: ) Advanced Emergency Braking System (Aebs)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Review on Driving Control Issues for Smart Electric Vehicles

Haque

Rahman²,

Islam

et al. 2021

IEEE Access

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“…Lane Departure Warning System (LDWS) (Chen & Boukerche, 2020). Both LKAS and LDWS contain road lane detection, which is one of the complex systems in ADAS.…”

Section: Introductionmentioning

confidence: 99%

Untitled

2021

JST

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In this work, we proposed High-Level Synthesis (HLS) optimization processes to improve the speed and the resource usage of complex algorithms, especially nested-loop. The proposed HLS optimization processes are divided into four steps: array sizing is performed to decrease the resource usage on Programmable Logic (PL) part, loop analysis is performed to determine which loop must be loop unrolling or loop pipelining, array partitioning is performed to resolve the bottleneck of loop unrolling and loop pipelining, and HLS interface is performed to select the best block level and port level interface for array argument of RTL design. A case study road lane detection was analyzed and applied with suitable optimization techniques to implement on the Xilinx Zynq-7000 family (Zybo ZC7010-1) which was a low-cost FPGA. From the experimental results, our proposed method reaches 6.66 times faster than the primitive method at clock frequency 100 MHz or about 6 FPS. Although the proposed methods cannot reach the standard real-time (25 FPS), they can instruct HLS developers for speed increasing and resource decreasing on an FPGA.

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