A vehicle type-based approach to model car following behaviors in simulation programs (case study: Car-motorcycle following behavior)

Amini, Ehsan; Tabibi, Masoud; Khansari, Ehsan Ramezani; Abhari, Mohammadreza

doi:10.1016/j.iatssr.2018.05.004

Cited by 26 publications

(11 citation statements)

References 22 publications

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“…This serves as clear evidence that highlights the importance of considering multiple stimulus parameters in motion planning. This finding also aligns with [13, 21, 36, 64].…”

Section: Performance Evaluationsupporting

confidence: 87%

“…

X_{2} normaland X_{3}

: Formulations of the repulsive potentials based on relative velocity and relative acceleration are done in the past [13, 16]. However, these formulations have a disadvantage; a large magnitude of negative relative velocity or negative relative acceleration may be possible due to the motion of the obstacles despite the EV being at rest, warranting reactions from EV, which is preposterous.…”

Section: Proposed Methodsmentioning

confidence: 99%

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2‐dimensional human‐like driver model for autonomous vehicles in mixed traffic

Sharath

Velaga

Quddus

2020

IET intell. transp. syst

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Classical artificial potential approach of motion planning is extended for emulating human driving behaviour in two dimensions. Different stimulus parameters including type of ego‐vehicle, type of obstacles, relative velocity, relative acceleration, and lane offset are used. All the surrounding vehicles are considered to influence drivers' decisions. No emphasis is laid on vehicle control; instead, an ego vehicle is assumed to reach the desired state. The study is on human‐like driving behaviour modelling. The developed motion planning algorithm formulates repulsive and attractive potentials in a data‐driven way in contrast to the classical arbitrary formulation. Interaction between the stimulus parameters is explicitly considered by using multivariate cumulative distribution functions. Comparison of two‐dimensional (lateral and longitudinal) performance indicators with a baseline model and generative adversarial networks indicate the effectiveness and suitability of the developed motion planning algorithm in the mixed traffic environment.

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“…This serves as clear evidence that highlights the importance of considering multiple stimulus parameters in motion planning. This finding also aligns with [13, 21, 36, 64].…”

Section: Performance Evaluationsupporting

confidence: 87%

“…

X_{2} normaland X_{3}

Section: Proposed Methodsmentioning

confidence: 99%

2‐dimensional human‐like driver model for autonomous vehicles in mixed traffic

Sharath

Velaga

Quddus

2020

IET intell. transp. syst

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show abstract

“…The same study also found that time gap is relatively consistent in different urban freeway roads, while it is different for rural roads [20]. A different study conducted in Iran [21] showed that a car driver tends to keep a longer distance with a lead motorcycle in comparison with a lead car, and seems to be more prudent to use the gas/brake pedal if gap distance is less than 10 m when following a motorcycle compared with a similar situation when following a car.…”

Section: ⅰ Introductionmentioning

confidence: 72%

Comparison of Car-Following Behavior in Terms of Safety Indicators Between China and Sweden

Tong

Selpi

2020

IEEE Trans. Intell. Transport. Syst.

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Understanding car-following behaviour in different countries is essential for the design and development of autonomous driving and further development of active safety systems that can function well worldwide, in particular in mixed traffic conditions. However, very few studies exist that compare car-following behaviours in different countries based on real driving data. This paper analyses the similarities and differences of drivers' car-following behaviour, in terms of time gap, gap distance, and time to collision, using both China and Sweden datasets from real road driving studies, in a bid to identify how these indicators affect drivers' speed control in car-following situations. Results indicate that the highest frequency of gap distance is observed in the same value-range in both datasets, while the highest frequency of time gap in the Sweden dataset is found at a lower value-range than the corresponding value-range in the China dataset. For both datasets, time gap is observed to be a more reliable indicator for car-following analysis than gap distance, since it is less sensitive to speed variations. Furthermore, time to collision (TTC) in the low travel speed ranges (v<50 km/h) tends to be steady in comparison with TTC at other speed ranges, so is the time gap in the high speed ranges (v>90 km/h). Therefore, time gap is recommended as the safety indicator for car-following analysis in high speed conditions, while a combination of time gap and TTC is recommended for low speed conditions, especially on urban roads.

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“…e use of the GM model to simulate the acceleration of the leading vehicle in an approach for identifying the leading vehicle was reported by Choudhury et al [33]. Amini et al [34] proposed a case study on carfollowing-motorcycle behaviour using the GHR model, which compares the longitudinal behaviour of a car following an erratic motorcycle to that of the car-following-car scenario. e finding demonstrated that longer headway is maintained at low speed during the motorcycle-car interaction compared to the car-car-following scenario.…”

Section: Journal Of Engineeringmentioning

confidence: 99%

Simulation Strategies for Mixed Traffic Conditions: A Review of Car-Following Models and Simulation Frameworks

Matcha

Namasivayam

Fouladi

et al. 2020

Journal of Engineering

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The area of traffic flow modelling and analysis that bridges civil engineering, computer science, and mathematics has gained significant momentum in the urban areas due to increasing vehicular population causing traffic congestion and accidents. Notably, the existence of mixed traffic conditions has been proven to be a significant contributor to road accidents and congestion. The interaction of vehicles takes place in both lateral and longitudinal directions, giving rise to a two-dimensional (2D) traffic behaviour. This behaviour contradicts with the traditional car-following (CF) or one-dimensional (1D) lane-based traffic flow. Existing one-dimensional CF models did the inclusion of lane changing and overtaking behaviour of the mixed traffic stream with specific alterations. However, these parameters cannot describe the continuous lateral manoeuvre of mixed traffic flow. This review focuses on all the significant contributions made by 2D models in evaluating the lateral and longitudinal vehicle behaviour simultaneously. The accommodation of vehicle heterogeneity into the car-following models (homogeneous traffic models) is discussed in detail, along with their shortcomings and research gaps. Also, the review of commercially existing microscopic traffic simulation frameworks built to evaluate real-world traffic scenario are presented. This review identified various vehicle parameters adopted by existing CF models and whether the current 2D traffic models developed from CF models effectively captured the vehicle behaviour in mixed traffic conditions. Findings of this study are outlined at the end.

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A vehicle type-based approach to model car following behaviors in simulation programs (case study: Car-motorcycle following behavior)

Cited by 26 publications

References 22 publications

2‐dimensional human‐like driver model for autonomous vehicles in mixed traffic

2‐dimensional human‐like driver model for autonomous vehicles in mixed traffic

Comparison of Car-Following Behavior in Terms of Safety Indicators Between China and Sweden

Simulation Strategies for Mixed Traffic Conditions: A Review of Car-Following Models and Simulation Frameworks

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