Adaptive Car Following Model

Mika, P.

doi:10.2478/scjme-2018-0041

Cited by 1 publication

(1 citation statement)

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“…where; d long saf e : the longitudinal safe distance, v long ego : the actual longitudinal velocity of ego vehicle, v long target : the longitudinal target velocity from the actor ahead, µ : the friction coefficient from road surface to ego vehicle tire, a max : the maximum feasible deceleration by ego vehicle that is proposed a value of -8 m/s 2 in [25], t gap : the minimum time gap to keep from actor ahead and the value 0.5s is proposed, which follows the hazardous value defined in [16], d long min : the minimum distance to keep when ego target velocity is 0 km/h, its value is proposed to 5m as suggested in [15]. To illustrate this metric, we consider a scenario with the ego vehicle driving at 80 km/h (∼22.2 m/s) with an actor ahead driving at 72 km/h (20 m/s).…”

Section: A Proposed Metricsmentioning

confidence: 99%

Detecting Hazardous Events: A Framework for Automated Vehicle Safety Systems

Xiao

Geiger

Yatbaz

et al. 2022

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

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The driving domain is inherently dangerous. To develop connected and automated vehicles that can detect potential sources of harm, we must clearly define these hazardous events and metrics to detect them. The majority of driving scenarios we face do not materialise harm, but we often face potentially hazardous near-miss scenarios. Potential harm is difficult to quantify when harm is not materialised; thus, few metrics detect these scenarios in the absence of collision and even fewer datasets label non-collision-based hazardous events. This study focuses on detecting near-miss scenarios due to other actors since human error is the primary source of harm. We first provide a concise overview of current event-specific metrics. We then propose an event-agnostic detection framework that exploits vehicle kinematics to detect evasive manoeuvres early and dynamically calculate minimum safe distances. Given inconsistent dataset labelling methods and collision-focused events, we provide a preliminary study to demonstrate an eventagnostic and configurable dataset annotation technique to label hazardous events, even when harm is not materialised. We show promising results detecting hazardous scenes on a labelled simulation benchmark, GTACrash.

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Section: A Proposed Metricsmentioning

confidence: 99%