Driving safety field theory modeling and its application in pre-collision warning system

Wang, Jianqiang; Wu, Jian; Zheng, Xunjia; Ni, Daiheng; Li, Keqiang

doi:10.1016/j.trc.2016.10.003

Cited by 182 publications

(58 citation statements)

References 33 publications

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“…According to [16], if all vehicles are ICVs, i.e. the MPR is 1, and there is a central controller that coordinate the braking force of every vehicle in real-time, we could define a metric to quantify the severity of a collision:…”

Section: ) Cooperative Collision Avoidance Algorithm Based On Minimumentioning

confidence: 99%

Longitudinal Safety Analysis For Heterogeneous Platoon of Automated And Human Vehicles

Yang

Wang

Pei

et al. 2018

2018 21st International Conference on Intelligent Transportation Systems (ITSC)

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With the recent advancement in environmental sensing, vehicle control and vehicle-infrastructure cooperation technologies, more and more autonomous driving companies start to put their intelligent cars into road test. But in the near future, we will face a heterogeneous traffic with both intelligent connected vehicles and human vehicles. In this paper, we investigated the impacts of four collision avoidance algorithms under different intelligent connected vehicles market penetration rate. A customized simulation platform is built, in which a platoon can be initiated with many key parameters. For every short time interval, the dynamics of vehicles are updated and input in a kinematics model. If a collision occurs, the energy loss is calculated to represent the crash severity. Four collision avoidance algorithms are chosen and compared in terms of the crash rate and severity at different market penetration rate and different locations of the platoon. The results generate interesting debates on the issues of heterogeneous platoon safety.

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Section: ) Cooperative Collision Avoidance Algorithm Based On Minimumentioning

confidence: 99%

Longitudinal Safety Analysis For Heterogeneous Platoon of Automated And Human Vehicles

Yang

Wang

Pei

et al. 2018

2018 21st International Conference on Intelligent Transportation Systems (ITSC)

Self Cite

View full text Add to dashboard Cite

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“…Moreover, the inverse reinforcement learning method was employed to learn the risk assessment model from driving demonstrations [14]. Some complex mathematic models were proposed considering dynamic features, such as the safety field model [19]. These models firstly defined the relationship between features and SA models and then calibrated the parameters of the function using realistic driving data [20].…”

Section: Related Workmentioning

confidence: 99%

Situational Assessments Based on Uncertainty-Risk Awareness in Complex Traffic Scenarios

et al. 2017

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Situational assessment (SA) is one of the key parts for the application of intelligent alternative-energy vehicles (IAVs) in the sustainable transportation. It helps IAVs understand and comprehend traffic environments better. In SA, it is crucial to be aware of uncertainty-risks, such as sensor failure or communication loss. The objective of this study is to assess traffic situations considering uncertainty-risks, including environment predicting uncertainty. According to the stochastic environment model, collision probabilities between multiple vehicles are estimated based on integrated trajectory prediction under uncertainty, which combines the physics-and maneuver-based trajectory prediction models for accurate prediction results in the long term. The SA method considers the probabilities of collision at different predicting points, the masses, and relative speeds between the possible colliding objects. In addition, risks beyond the prediction horizon are considered with the proposition of infinite risk assessments (IRAs). This method is applied and proved to assess risks regarding unexpected obstacles in traffic, sensor failure or communication loss, and imperfect detections with different sensing accuracies of the environment. The results indicate that the SA method could evaluate traffic risks under uncertainty in the dynamic traffic environment. This could help IAVs' plan motion trajectories and make high-level decisions in uncertain environments.

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“…Moreover, the pressure field was simulated to visualize its impact on vehicle movement [19]. These studies helped to quantify the impact of road infrastructures on drivers, and such impact was also known as driving safety field proposed by Wang et al [20,21]. In driving safety field, road infrastructures were the sources of static potential field that had an important role in collision warning algorithm.…”

Section: Introductionmentioning

confidence: 99%

A New Method Based on Field Strength for Road Infrastructure Risk Assessment

Chen

2018

Journal of Advanced Transportation

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Because road infrastructures have significant impact on driving safety, their risk levels need to be evaluated dynamically according to drivers’ perception. To achieve this, this paper proposes two field strength models to quantify the impact of road infrastructures on drivers. First, road infrastructures are classified into two types (continuous and discrete). Then, two field strength models for these types are proposed. Continuous field strength model describes the impact of long-belt-shape infrastructure by differential and integral methods. Discrete field strength model describes the static and dynamic characteristics of infrastructures. This model includes four parameters: mass of vehicles, mass of infrastructures, warning level, and kinetic energy of road infrastructures. The field strength is a relative concept, which changes with vehicle state. At the end of this paper, risk assessment principles are listed to clarify the nature of road infrastructure risk evaluation. A workflow of risk assessment and a case study are presented to illustrate the application of this novel method. The result of this study shows that ① the field strength is positively related to its risk level; ② the distribution of road infrastructure risks explains driver behaviour correctly; ③ drivers tend to keep driving in low-risk area. These findings help to explain the impact mechanism of road infrastructures on drivers, which can be applied in AI-based driving assistance system in the future.

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Driving safety field theory modeling and its application in pre-collision warning system

Cited by 182 publications

References 33 publications

Longitudinal Safety Analysis For Heterogeneous Platoon of Automated And Human Vehicles

Longitudinal Safety Analysis For Heterogeneous Platoon of Automated And Human Vehicles

Situational Assessments Based on Uncertainty-Risk Awareness in Complex Traffic Scenarios

A New Method Based on Field Strength for Road Infrastructure Risk Assessment

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