Evaluating impacts of different longitudinal driver assistance systems on reducing multi-vehicle rear-end crashes during small-scale inclement weather

Li, Ye; Xing, Lu; Wang, Wei; Wang, Hao; Dong, Changyin; Liu, Shan-Wen

doi:10.1016/j.aap.2017.07.014

Cited by 47 publications

(25 citation statements)

References 60 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…detect relative distance and velocity between the host vehicle and the target vehicle, according to the characteristics of vehicle radar [24], its measurement uncertainties could be set to be:…”

Section: Measurement Uncertainties Vehicle Sensorsmentioning

confidence: 99%

See 1 more Smart Citation

Vehicle‐localization‐based and DSRC‐based autonomous vehicle rear‐end collision avoidance concerning measurement uncertainties

Chen

Zhan

et al. 2019

IEEJ Transactions Elec Engng

View full text Add to dashboard Cite

Rear‐end collision is one of the most serious accidents for vehicles, some rear end collision avoidance systems have been developed to protect vehicles from rear‐end collisions. Current rear‐end collision avoidance systems mainly utilize vehicle‐relative measurement sensors to acquire information on surrounding vehicles, considering limited detection ranges and impaired performance in bad weathers of these vehicle sensors, currently used collision avoidance systems cannot guarantee vehicle safety in all environments. Vehicle‐localization‐based and intervehicle communication‐based rear‐end collision avoidance approaches can be possible solutions to improve vehicle safety compared with present vehicle sensor‐based collision avoidance systems. Dedicated Short‐Range Communication (DSRC) is introduced to transfer information on surrounding vehicles instead of using vehicle‐relative measurement sensors. Vehicle dynamic model and brake system dynamics are used to generate precise vehicle states that would be used in rear‐end collision avoidance. Required safe distance is used as the safety index when using different vehicle‐localization methods with a predefined safety probability of 99.99%. Monte Carlo simulations are executed to evaluate the influence of measurement uncertainties. Simulation results show that vehicle‐localization‐based and DSRC‐based rear‐end collision avoidance methods can guarantee vehicles free from rear‐end collision and expand the service capabilities of velocity compared with the traditional vehicle radar‐based rear‐end collision avoidance method. Evaluation outcomes encourage us to adopt a more precise absolute vehicle‐localization approach in rear‐end collision avoidance, because required safe distance and safe distance margin can be decreased when using a high‐precision vehicle‐localization method compared with standalone GNSS‐based localization. Some further aspects are discussed in this work that need to be tackled with to make vehicle‐localization‐based and intervehicle communication‐based rear‐end collision avoidance systems into practice, including real‐time brake system dynamics acquirement, benefit of DSRC to curve roads, and so on. © 2019 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

show abstract

Section: Measurement Uncertainties Vehicle Sensorsmentioning

confidence: 99%

“…Comparing (22) with (24), measurement uncertainties between radar and high-precision vehicle-localization methods might be similar, however, their measurements are different, as the former is relative measurements, and others are absolute measurements.…”

Section: Measurement Uncertainties Vehicle Sensorsmentioning

confidence: 99%

Vehicle‐localization‐based and DSRC‐based autonomous vehicle rear‐end collision avoidance concerning measurement uncertainties

Chen

Zhan

et al. 2019

IEEJ Transactions Elec Engng

View full text Add to dashboard Cite

show abstract

“…In fact, a well-designed HMI has the potential to provide CV drivers with proactive decision-making supports so that CV drivers could more timely respond to an imminent hazardous traffic condition and, thus, reduce the probability of involvement in traffic collisions. However, inappropriate integration of various CV warnings and advisories may mislead, distract, or even disturb drivers from their normal driving task ( Li et al, 2017 ; Talamonti et al, 2017 ). These adverse effects are particularly significant during high-workload situations or driving under inclement weather and road surface conditions.…”

Section: Introductionmentioning

confidence: 99%

Assessment of Drivers’ Perceptions of Connected Vehicle–Human Machine Interface for Driving Under Adverse Weather Conditions: Preliminary Findings From Wyoming

2020

View full text Add to dashboard Cite

Connected vehicle (CV) technology aims to improve drivers' situational awareness through audible and visual warnings displayed on a human-machine interface (HMI), thus reducing crashes caused by human error. This paper developed a driving simulator test bed to assess the readability and usefulness of the Wyoming CV applications. A total number of 26 professional drivers were recruited to participate in a driving-simulator study. Prior to driving the simulator, the participants were trained on both the concept of CV technology and the developed CV applications as well as the operation of the driving simulator. Three driving simulation scenarios were designed. For each scenario, participants drove two times: one with the HMI turned on and another one with the HMI turned off. After driving the simulator, a comprehensive revealed-preference survey was employed to collect the participants' perceptions of CV technology and Wyoming CV applications. Results show that the Wyoming CV applications were most favored under poor-visibility driving conditions. Among the Wyoming CV applications, forward collision warning and rerouting applications were experienced as the most useful. Approximately 89% of the participants stated that the Wyoming CV applications provided them with improved road condition information and increased their experienced safety while driving; 65% of the participants stated the CV applications and the HMI did not introduce distraction from the primary task of driving. Finally, this paper concludes that the design of CV HMI needs to balance a trade-off between the readability of the warnings and drivers' capability to safely recognize and timely respond to the received warnings.

show abstract

“…Some risk prediction and evaluation models, such as references [11][12][13][14] were proposed, applying macroscopic traffic flow data including density, volume, etc., to proactively perceive potential risk. For example, reference [15] attempted to utilize different driver assistance systems to better reduce small-scale inclement weather-caused rear-end crashes. Two disturbance-based indices were then proposed in order to represent the general safety level of car-following scenarios [16].…”

Section: Introductionmentioning

confidence: 99%

Unravelling the Impacts of Parameters on Surrogate Safety Measures for a Mixed Platoon

et al. 2020

View full text Add to dashboard Cite

With the precedence of connected automated vehicles (CAVs), car-following control technology is a promising way to enhance traffic safety. Although a variety of research has been conducted to analyze the safety enhancement by CAV technology, the parametric impact on CAV technology has not been systematically explored. Hence, this paper analyzes the parametric impacts on surrogate safety measures (SSMs) for a mixed vehicular platoon via a two-level analysis structure. To construct the active safety evaluation framework, numerical simulations were constructed which can generate trajectories for different kind of vehicles while considering communication and vehicle dynamics characteristics. Based on the trajectories, we analyzed parametric impacts upon active safety on two different levels. On the microscopic level, parameters including controller dynamic characteristics and equilibrium time headway of car-following policies were analyzed, which aimed to capture local and aggregated driving behavior’s impact on the vehicle. On the macroscopic level, parameters incorporating market penetration rate (MPR), vehicle topology, and vehicle-to-vehicle environment were extensively investigated to evaluate their impacts on aggregated platoon level safety caused by inter-drivers’ behavioral differences. As indicated by simulation results, an automated vehicle (AV) suffering from degradation is a potentially unsafe component in platoon, due to the loss of a feedforward control mechanism. Hence, the introduction of connected automated vehicles (CAVs) only start showing benefits to platoon safety from about 20% CAV MPR in this study. Furthermore, the analysis on vehicle platoon topology suggests that arranging all CAVs at the front of a mixed platoon assists in enhancing platoon SSM performances.

show abstract

Evaluating impacts of different longitudinal driver assistance systems on reducing multi-vehicle rear-end crashes during small-scale inclement weather

Cited by 47 publications

References 60 publications

Vehicle‐localization‐based and DSRC‐based autonomous vehicle rear‐end collision avoidance concerning measurement uncertainties

Vehicle‐localization‐based and DSRC‐based autonomous vehicle rear‐end collision avoidance concerning measurement uncertainties

Assessment of Drivers’ Perceptions of Connected Vehicle–Human Machine Interface for Driving Under Adverse Weather Conditions: Preliminary Findings From Wyoming

Unravelling the Impacts of Parameters on Surrogate Safety Measures for a Mixed Platoon

Contact Info

Product

Resources

About