STAG: A novel interaction-aware path prediction method based on Spatio-Temporal Attention Graphs for connected automated vehicles

Azadani, Mozhgan Nasr; Boukerche, Azzedine

doi:10.1016/j.adhoc.2022.103021

Cited by 11 publications

(13 citation statements)

References 30 publications

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“…From the 25th penetration rate, the delay started to drop down to the 100th penetration rate. From the 75th penetration rate, the delay attained by HGE-ARCO scheme dropped much lesser over PRO, ARCO, HHO, BOA, HGS, BES, FDA + DP [1], NN [5] and STAG [6] schemes. At the 100th penetration rate, the delay attained by HGE-ARCO scheme is around 1.5.…”

Section: Analysis Of Delay and Deviationmentioning

confidence: 95%

“…This is well accomplished by our HGE-ARCO scheme. At first, from 0th to 25th penetration rates, the delay is higher for all HGE-ARCO schemes as well as PRO, ARCO, HHO, BOA, HGS, BES, FDA + DP [1], NN [5], and STAG [6] schemes. From the 25th penetration rate, the delay started to drop down to the 100th penetration rate.…”

Section: Analysis Of Delay and Deviationmentioning

confidence: 97%

“…The study on proposed HGE-ARCO for path planning was done in MATLAB. The assessment was done over PRO (Poor Rich Optimization), ARCO, HHO (Harris Hawks Optimization), BOA (Butterfly Optimization), HGS (Hunger Games Search), BES (Bald Eagle Search), FDA + DP [1], NN [5] and STAG [6] for assorted measures.…”

Section: Simulation Proceduresmentioning

confidence: 99%

“…The performance of the HGE-ARCO scheme for automated vehicle path planning is evaluated over PRO, ARCO, HHO, BOA, HGS, BES, FDA + DP [1], NN [5], and STAG [6] for extensive metrics such as delay and deviation. The outcomes for delay and deviation are plotted in Figures 4a and 4b in that order.…”

Section: Analysis Of Delay and Deviationmentioning

confidence: 99%

“…The spline curve is a straightforward, acceptable, and practical geometry computation tool that adapts to simple situations. [6][7][8]. Even if geometric approaches offer certain benefits, they cannot make up for their drawbacks.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

HOPAV: Hybrid optimization‐oriented path planning for non‐connected and connected automated vehicles

Arumugam¹,

Kavitha

Prado

et al. 2023

IET Control Theory & Appl

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Over the past ten years, autonomous driving has garnered a great deal of interest from both the scientific community and business. Strong technological advancements have made automated driving more practical because human driving abilities seem limited in terms of driving experience, reaction time, and the effectiveness of real‐time decisions. The development of highly autonomous driving algorithms is inextricably tied to planning and changing a vehicle path that must be user‐acceptable, efficient, and collision‐free. Path planning for road vehicles is a difficult problem due to the high speed involved and the requirement to assure passenger safety. Here, a new path‐planning method is developed for both connected and disconnected automatic road vehicles on multilane highways. This paradigm states that the right phrases to describe the objectives of vehicle improvement, passenger comfort, prevention of vehicle‐to‐vehicle collisions and road deviations are included in the objective function. Hunger Games improved Archimedes optimization (HGE‐ARCO) is used to optimize the paths for achieving better‐planned outcomes. At the 100th penetration rate, the HGE‐ARCO scheme reached a top speed of about 99 km/h. The results shows unmistakably that the proposed HGE‐ARCO produces a time of 12.3021 s, which is less than other conventional methods.

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Section: Analysis Of Delay and Deviationmentioning

confidence: 95%