Traffic Dynamics at Intersections Subject to Random Misperception

Berkhahn, Volker; Kleiber, Marcel; Langner, Johannes; Timmermann, Chris; Weber, Stefan

doi:10.1109/tits.2020.3045480

Cited by 7 publications

(6 citation statements)

References 35 publications

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“…The authors argue that a photorealistic simulation is not necessary to study the AV behavior as long as the input to the DP is affected by the same error distribution as the real one. Berkhahn et al [29] conducted a simulation study injecting errors, modeled with random ordinary differential equations (RODEs), in the simulation pipeline to study their impact on intersection conflicts. Interestingly, the perception errors modeled are intended for human drivers and not for an S&P of an AV.…”

Section: B Modeling Errorsmentioning

confidence: 99%

CoPEM: Cooperative Perception Error Models for Autonomous Driving

Piazzoni

Cherian

Vijay

et al. 2022

2022 IEEE 25th International Conference on Intelligent Transportation Systems (ITSC)

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In this paper, we introduce the notion of Cooperative Perception Error Models (coPEMs) towards achieving an effective and efficient integration of V2X solutions within a virtual test environment. We focus our analysis on the occlusion problem in the (onboard) perception of Autonomous Vehicles (AV), which can manifest as misdetection errors on the occluded objects. Cooperative perception (CP) solutions based on Vehicle-to-Everything (V2X) communications aim to avoid such issues by cooperatively leveraging additional points of view for the world around the AV. This approach usually requires many sensors, mainly cameras and LiDARs, to be deployed simultaneously in the environment either as part of the road infrastructure or on other traffic vehicles. However, implementing a large number of sensor models in a virtual simulation pipeline is often prohibitively computationally expensive. Therefore, in this paper, we rely on extending Perception Error Models (PEMs) to efficiently implement such cooperative perception solutions along with the errors and uncertainties associated with them. We demonstrate the approach by comparing the safety achievable by an AV challenged with a traffic scenario where occlusion is the primary cause of a potential collision.

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Section: B Modeling Errorsmentioning

confidence: 99%

CoPEM: Cooperative Perception Error Models for Autonomous Driving

Piazzoni

Cherian

Vijay

et al. 2022

2022 IEEE 25th International Conference on Intelligent Transportation Systems (ITSC)

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“…Widely-used traffic simulations like PTV Vissim (see [119], [120], [121]), SUMO (see [122], [123]), PELOPS (see [24]), or IPG Carmaker (see [124]), are able to simulate traffic flow, but with limitations on the insight of driver behavior modeling. Studies that use these traffic simulations to model critical and accident situations are (so far) tests of feasibility rather than actual simulations of real-world conflict situations (see [35], [125], [126], [127], [128]). Subsequently, a classification of currently used modules in driver behavior models is presented.…”

Section: A Driver Behavior Modulesmentioning

confidence: 99%

“…Noise Models aim to reproduce the human error by adding a variance (noise) to a value, e.g., a stochastically generated error to the perceived distance, speed, or acceleration (see [128], [162]). This contradicts Lee's τ -theory [107], according to which humans use the change in the optical image at the driver's retina for TTC determination instead of using speed, distance, and acceleration estimates for longitudinal guidance.…”

Section: A Driver Behavior Modulesmentioning

confidence: 99%

“…Almost no driver model simulates the actual perception process, including the UFOV (the viewing area where information can aggregated). Instead, agents have direct access to all vehicles and noise is subsequently added or kinematic values are not updated temporarily (see [58], [125], [127], [128], [162]). There are only a few models that simulate the UFOV (see [19], [116], [165], [166], [181]).…”

Section: Application Of Human Cognition and Error In Driver Modelsmentioning

confidence: 99%

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What Do Traffic Simulations Have to Provide for Virtual Road Safety Assessment? Human Error Modeling in Traffic Simulations

Siebke

Mai

Prokop

2022

IEEE Trans. Intell. Transport. Syst.

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Will Advanced Driving Assistance Systems (ADAS) and Highly Automated Driving (HAD) perform in the expected manner? Will they actually make road traffic safer, or will they potentially introduce new critical situations or road accidents? It is almost impossible to address these questions solely through real-world tests. A promising tool to provide appropriate answers in a time-and cost-efficient way without exposing subjects to risk are virtual assessment methods. Reliable safety assessments are only possible, if the traffic simulations provide realistic traffic, including critical situations and road accidents. This paper provides a review of how human error contributes to critical situations and accidents in road traffic. The focus is on the causes and mechanisms of human error, which driver behavior models must address in order to simulate realistic traffic. For this purpose, Rasmussen's error taxonomies are applied to the traffic context and extended with further research. The paper shows the causes of those human errors and that the underlying mechanisms thereof should be taken into account in order to obtain more transparent and realistic driver behavior models. It is shown, which concepts for modelling realistic traffic exist and how virtual safety assessment could benefit from this development. In addition, the driver behavior model DReaM (Driver Reaction Model) is presented to address the issues resulting from existing cognitive driver models.

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“…Furthermore, the interaction between perceptual error and time headway on vehicles driving freely was studied in [ 26 ], and the results show that a smaller headway time distance exacerbates the effect of perceptual errors. The effect of vehicle perception error under multiple intersections was studied in [ 27 ], and the model captures the real world trade-off between safety and efficiency for potential future traffic systems. On the other hand, related works have already been studied on improving the accurate perception of the vehicle.…”

Section: Introductionmentioning

confidence: 99%

Cooperative Intersection with Misperception in Partially Connected and Automated Traffic

et al. 2021

Sensors

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The emerging connected and automated vehicle (CAV) has the potential to improve traffic efficiency and safety. With the cooperation between vehicles and intersection, CAVs can adjust speed and form platoons to pass the intersection faster. However, perceptual errors may occur due to external conditions of vehicle sensors. Meanwhile, CAVs and conventional vehicles will coexist in the near future and imprecise perception needs to be tolerated in exchange for mobility. In this paper, we present a simulation model to capture the effect of vehicle perceptual error and time headway to the traffic performance at cooperative intersection, where the intelligent driver model (IDM) is extended by the Ornstein–Uhlenbeck process to describe the perceptual error dynamically. Then, we introduce the longitudinal control model to determine vehicle dynamics and role switching to form platoons and reduce frequent deceleration. Furthermore, to realize accurate perception and improve safety, we propose a data fusion scheme in which the Differential Global Positioning system (DGPS) data interpolates sensor data by the Kalman filter. Finally, a comprehensive study is presented on how the perceptual error and time headway affect crash, energy consumption as well as congestion at cooperative intersections in partially connected and automated traffic. The simulation results show the trade-off between the traffic efficiency and safety for which the number of accidents is reduced with larger vehicle intervals, but excessive time headway may result in low traffic efficiency and energy conversion. In addition, compared with an on-board sensor independently perception scheme, our proposed data fusion scheme improves the overall traffic flow, congestion time, and passenger comfort as well as energy efficiency under various CAV penetration rates.

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Traffic Dynamics at Intersections Subject to Random Misperception

Cited by 7 publications

References 35 publications

CoPEM: Cooperative Perception Error Models for Autonomous Driving

CoPEM: Cooperative Perception Error Models for Autonomous Driving

What Do Traffic Simulations Have to Provide for Virtual Road Safety Assessment? Human Error Modeling in Traffic Simulations

Cooperative Intersection with Misperception in Partially Connected and Automated Traffic

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