Stochastic driver speed control behavior modeling in urban intersections using risk potential-based motion planning framework

Akagi, Yasuhiro; Raksincharoensak, Pongsathorn

doi:10.1109/ivs.2015.7225713

Cited by 19 publications

(12 citation statements)

References 15 publications

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“…Intersection crossing under occluded conditions was also addressed in the limited-visibility and uncertainty-aware motion planning method proposed in [25], in which the planner assumed the presence of a hypothetical vehicle traveling at a certain constant speed from outside of the perceptive field, and then planned the motion of the ego vehicle accordingly. In Refernece [26], proactive braking prior to entering occluded intersections without traffic signals was modeled using a potential risk function, with expert driving data being used to estimate the parameters of the function. In References [27,28], approaches for determining the safe speed at blind intersections were proposed, which were based on intersection geometry and the position of the ego vehicle, under the assumption that an undetected traffic participant was heading toward the intersection from outside of the visible area.…”

Section: Model-based Approachesmentioning

confidence: 99%

Deadlock-Free Planner for Occluded Intersections Using Estimated Visibility of Hidden Vehicles

et al. 2021

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A common approach used for planning blind intersection crossings is to assume that hypothetical vehicles are approaching the intersection at a constant speed from the occluded areas. Such an assumption can result in a deadlock problem, causing the ego vehicle to remain stopped at an intersection indefinitely due to insufficient visibility. To solve this problem and facilitate safe, deadlock-free intersection crossing, we propose a blind intersection planner that utilizes both the ego vehicle and the approaching vehicle’s visibility. The planner uses a particle filter and our proposed visibility-dependent behavior model of approaching vehicles for predicting hidden vehicles. The behavior model is designed based on an analysis of actual driving data from multiple drivers crossing blind intersections. The proposed planner was tested in a simulation and found to be effective for allowing deadlock-free crossings at intersections where a baseline planner became stuck in a deadlock. The effects of perception accuracy and sensor position on output motion were also investigated. It was found that the proposed planner delayed crossing motion when the perception was imperfect. Furthermore, our results showed that the planner decelerated less while crossing the intersection with the front-mounted sensor configuration compared to the roof-mounted configuration due to the improved visibility. The minimum speed difference between the two sensor configurations was 1.82 m/s at an intersection with relatively poor visibility and 1.50 m/s at an intersection with good visibility.

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Section: Model-based Approachesmentioning

confidence: 99%

Deadlock-Free Planner for Occluded Intersections Using Estimated Visibility of Hidden Vehicles

et al. 2021

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“…The driver models of braking behavior based on a potential field model are proposed [4] [5] [6] [7] [8]. Wolf et al proposed an artificial potential field model for avoiding other vehicles and keeping a traffic lane in the highway [4].…”

Section: B Risk Potential Model For Motion Planningmentioning

confidence: 99%

“…We select the arc-tangent function (9) to represent the braking behavior (the advantages of this model have been discussed in [6]). Its parameters are the scaling factor σ and the offset of the center of the intersection μ.…”

Section: Risk Potential Model For the Unsignalized Intersectionsmentioning

confidence: 99%

“…Since the evaluation value E is the average deceleration speed to stop the vehicle before the intersection, the value E should be under 6 [20]. The results of the instructors and general drivers of both the priority side and non-priority side is under 6 . However, the results of the half of the elderly drivers are over the speed.…”

Section: A Evaluation Value Based On the Kinematic Parameters Of Thementioning

confidence: 99%

“…Authors have been proposed the driver assistance system to recommend the appropriate speed to pass the urban unsignalized intersection [6]. The speed control model of the system is created by analyzing the actual driving data of expert drivers.…”

Section: Introductionmentioning

confidence: 99%

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An analysis of an elderly driver behaviour in urban intersections based on a risk potential model

Akagi

Raksincharoensak

2015

IECON 2015 - 41st Annual Conference of the IEEE Industrial Electronics Society

Self Cite

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This paper proposes a driving safety evaluation method based on the parameter analysis of a driver model. Since the driving abilities, such as reaction, recognition and judgment time decrease with aging, it is difficult for elderly drivers to avoid the traffic accidents instantly. Therefore, the proactive safety driving is important for the elderly drivers to prevent the accidents. Then, the safety evaluation criteria of a driving behavior are useful to encourage the driver to keep proactive safety driving. And these criteria are useful to develop an advanced driver assistance system for elderly drivers. To evaluate the driving behavior, we use the parameters of a risk potential model which is the driver model to represent the speed and course control around the risk factors. Since the risk potential model represents the driving behavior by using a small number of the parameters, we make the assumption that the parameters are highly abstract the driving behavior and it is useful for analyzing the safety level of the driver. To evaluate the assumption experimentally, we collect the actual driving data of elderly, general and normative drivers for analyzing the parameters of the risk potential model and the safety level of the driver. Finally, the results show that the parameters of the risk potential model can be used to indicate the features of the driving behavior of the elderly drivers.

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Automated Driving Vehicles

Song

2021

Vehicle Dynamics

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This chapter introduces a full scope of autonomous vehicle technology ranging from perception to control. While a literature review for commercial environment sensors and sensor fusion to both model-based and data-driven approaches is summarized in Sect. 2, detailed decision and motion planning strategies for urban driving situations as well as highway are described. Moreover, some experimental results are demonstrated in Sect. 3. In Sects. 4 and 5, lateral and longitudinal control methods are proposed respectively in the hierarchical structure. The several upper controllers are designed to track the desired path and their performances are compared experimentally. The torque controller for a steering actuator as a lower controller is designed based on nonlinear control techniques. Similarly, the upper and lower controller for longitudinal control with consideration of human driver characteristics is proposed for better naturalistic driving performance. Finally, a scenario-based approach for validation of the autonomous vehicle technology including all proposed algorithms is investigated in the end.

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Stochastic driver speed control behavior modeling in urban intersections using risk potential-based motion planning framework

Cited by 19 publications

References 15 publications

Deadlock-Free Planner for Occluded Intersections Using Estimated Visibility of Hidden Vehicles

Deadlock-Free Planner for Occluded Intersections Using Estimated Visibility of Hidden Vehicles

An analysis of an elderly driver behaviour in urban intersections based on a risk potential model

Automated Driving Vehicles

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