A Vehicle Type Dependent Car-following Model Based on Naturalistic Driving Study

Wu, Ping; Gao, Feng; Li, Keqiang

doi:10.3390/electronics8040453

Cited by 19 publications

(8 citation statements)

References 40 publications

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“…Where, 𝑐 𝑙𝑜𝑛 is the virtual damping coefficient in the model, 𝑘 𝑙𝑜𝑛 is the virtual spring stiffness coefficient in the model, 𝑥 0 is the original length of the virtual spring corresponding to different speeds, and its physical meaning is the best following distance. The calculation algorithm of 𝑥 0 is referred to [47]. At time 0, the 𝑈 𝑥,0 = 0 .…”

Section: 𝐹 𝑥 ∝ 𝑘 1 δ𝑥mentioning

confidence: 99%

Humanlike Decision and Motion Planning for Expressway Lane Changing Based on Artificial Potential Field

Gao

2022

IEEE Access

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The autonomous vehicles (AVs) need to share the driving environment with the human driving vehicles (HDVs) on expressway in the future. The non-humanlike lane changing (LC) behavior of AVs can mislead human drivers, which brings potential risks. Stronger humanlike ability requires a more complex algorithm. However, the requirement of the on-board vehicle computation resources limits the humanlike ability of LC algorithms. In this context, considering environmental risks, driver speed requirements and driver focus shifting process, this paper proposes a new type of LC algorithm based on artificial potential field (APF) aiming to improve the humanlike ability. The coupling relationship between the longitudinal and the lateral potential field forces is analyzed theoretically based on environmental risk APF. Based on the relationship, we proposed a conversion mechanism of the driver between the lateral and longitudinal potential field forces to mimic the driver's speed requirement. Then a target lane selection strategy is designed to trade-off the driver's speed requirement and safety between multiple lanes. Finally, to mimic the driver's focus transfer process, the lateral forces of the ego vehicle are analyzed theoretically to further propose a moving virtual lane lines algorithm to build the lateral space varying potential field. The algorithm proposed in this paper is validated by comparing with other LC algorithms in the real traffic scenarios. The results indicate that the proposed algorithm has a better ability to mimic human driver's LC decision-making and provides a smoother and safer humanlike trajectory.INDEX TERMS Autonomous vehicles, artificial potential field, lane changing decision-making, motion planning.

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Section: 𝐹 𝑥 ∝ 𝑘 1 δ𝑥mentioning

confidence: 99%

Humanlike Decision and Motion Planning for Expressway Lane Changing Based on Artificial Potential Field

Gao

2022

IEEE Access

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“…For the development of advanced driver assistance systems, such as forward collision warning system and lane departure warning system, several types of indexes have been proposed to determine the trigger time of warning. In those researches, the time to collision (TTC) [30] and the time to line crossing (TLC) [33] are widely used for the evaluation of longitudinal and lateral driving risk, respectively. Therefore, in this paper, TTCi (TTC is replaced by TTCi to avoid dividing by zero) and TLC are used to measure the driving risk:…”

Section: A Measurement Index Of Driving Capabilitymentioning

confidence: 99%

“…For instance, it is found from the studies of Liang et al that the lateral driving ability of driver may be improved by cognitive distraction [29]. Moreover, each driver has its own driving manner [30] and so a cooperative driving system should be adaptable to various drivers.…”

Section: Introductionmentioning

confidence: 99%

Driving Capability-Based Transition Strategy for Cooperative Driving: From Manual to Automatic

2020

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In open traffic environments, humans still have to remain in the control loop of vehicle due to the insufficient of the existing technologies and their high costs. For the realization of cooperation between the human and the automatic driving system, the determination of the time when automatic driving is necessary is very important. To avoid unnecessary intervention when the driver has the control authority of vehicle, a new driving capability-based transition strategy was proposed, which comprehensively considers the driver's correction ability and the driving risk. The transition time from the human driver to the automatic driving system is determined by an unreliable domain (UD), whose boundary is modeled according to the driving data recorded by a driving simulator and statistically described by a log-normal distribution. Furthermore, an adaptive algorithm is designed to update the parameters of UD boundary online to make this strategy suitable for different drivers. This UD-based transition strategy is validated by several tests on the driving simulator. The bench test results show that the individual driving characteristic can be identified by the adaptive algorithm in time, the transition time determined by UD is more accurate, and sufficient time is reserved for the correction carried out by the automatic driving system. INDEX TERMS Automatic driving, human-machine cooperation, driving capability, correction ability, driving risk.

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“…The current issue and full text archive of this journal is available on Emerald Insight at: https://www.emerald.com/insight/2399-9802.htm and manipulates the merging model based on factors such as desired gap distance and desired mainstream speed. Basically, this type of algorithm defines the model in a form of preferred and actual accelerations and distance gap maintain (Awal et al, 2013a(Awal et al, , 2013bChou et al, 2016;Karimi et al, 2020;Wu et al, 2019). An advanced form of the second type is to consider multiple optimization targets and generate the reference merging path by solving the optimal solution.…”

Section: Introductionmentioning

confidence: 99%

Effects of connected and autonomous vehicle merging behavior on mainline human-driven vehicle

Yue

Abdel-Aty

Wang

2021

JICV

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Purpose This study aims to evaluate the influence of connected and autonomous vehicle (CAV) merging algorithms on the driver behavior of human-driven vehicles on the mainline. Design/methodology/approach Previous studies designed their merging algorithms mostly based on either the simulation or the restricted field testing, which lacks consideration of realistic driving behaviors in the merging scenario. This study developed a multi-driver simulator system to embed realistic driving behavior in the validation of merging algorithms. Findings Four types of CAV merging algorithms were evaluated regarding their influences on driving safety and driving comfort of the mainline vehicle platoon. The results revealed significant variation of the algorithm influences. Specifically, the results show that the reference-trajectory-based merging algorithm may outperform the social-psychology-based merging algorithm which only considers the ramp vehicles. Originality/value To the best of the authors’ knowledge, this is the first time to evaluate a CAV control algorithm considering realistic driver interactions rather than by the simulation. To achieve the research purpose, a novel multi-driver driving simulator was developed, which enables multi-drivers to simultaneously interact with each other during a virtual driving test. The results are expected to have practical implications for further improvement of the CAV merging algorithm.

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A Vehicle Type Dependent Car-following Model Based on Naturalistic Driving Study

Cited by 19 publications

References 40 publications

Humanlike Decision and Motion Planning for Expressway Lane Changing Based on Artificial Potential Field

Humanlike Decision and Motion Planning for Expressway Lane Changing Based on Artificial Potential Field

Driving Capability-Based Transition Strategy for Cooperative Driving: From Manual to Automatic

Effects of connected and autonomous vehicle merging behavior on mainline human-driven vehicle

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