Assessing the benefit of the brake assist system for pedestrian injury mitigation through real-world accident investigations

Badea-Romero, Alexandro; Páez, Francisco Javier; Furones, Arturo; Barrios, José Manuel Vaquero; de-Miguel, Juan L.

doi:10.1016/j.ssci.2012.10.004

Cited by 14 publications

(7 citation statements)

References 4 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…If certain safety-critical thresholds for the interaction are exceeded and the driver does not take any evasive action, AEB systems take active control of the brakes and, in some cases, other vehicles' subsystems (steering wheel, throttle, suspension, etc. ), thereby reducing fatalities, severity of injuries, and social costs [5,6]. However, AEB systems generally consist of three subsystems or levels, namely "perception", "decision making", and "execution", which can have quite different performance depending on the vendor/supplier that has developed it and the sensor technology used to acquire the scene data.…”

Section: Introductionmentioning

confidence: 99%

Surrogate Safety Measures Prediction at Multiple Timescales in V2P Conflicts Based on Gated Recurrent Unit

et al. 2021

View full text Add to dashboard Cite

Improving pedestrian safety at urban intersections requires intelligent systems that should not only understand the actual vehicle–pedestrian (V2P) interaction state but also proactively anticipate the event’s future severity pattern. This paper presents a Gated Recurrent Unit-based system that aims to predict, up to 3 s ahead in time, the severity level of V2P encounters, depending on the current scene representation drawn from on-board radars’ data. A car-driving simulator experiment has been designed to collect sequential mobility features on a cohort of 65 licensed university students who faced different V2P conflicts on a planned urban route. To accurately describe the pedestrian safety condition during the encounter process, a combination of surrogate safety indicators, namely TAdv (Time Advantage) and T2 (Nearness of the Encroachment), are considered for modeling. Due to the nature of these indicators, multiple recurrent neural networks are trained to separately predict T2 continuous values and TAdv categories. Afterwards, their predictions are exploited to label serious conflict interactions. As a comparison, an additional Gated Recurrent Unit (GRU) neural network is developed to directly predict the severity level of inner-city encounters. The latter neural model reaches the best performance on the test set, scoring a recall value of 0.899. Based on selected threshold values, the presented models can be used to label pedestrians near accident events and to enhance existing intelligent driving systems.

show abstract

Section: Introductionmentioning

confidence: 99%

Surrogate Safety Measures Prediction at Multiple Timescales in V2P Conflicts Based on Gated Recurrent Unit

et al. 2021

View full text Add to dashboard Cite

show abstract

“…As pedestrians are composed of a wide variety of sizes, move in all directions, and wear clothes made of different clothing materials, their appearances constitute more variables than cars to AEB systems. It is critically to ensure that such PAEB systems are capable of performing adequately in a wide range of scenarios to offer a potential reduction of VRUs fatalities and injuries [10]. An AEB system consists of perception level, decision-making level, and execution level subsystems.…”

Section: Introductionmentioning

confidence: 99%

Development of Test Equipment for Pedestrian-Automatic Emergency Braking Based on C-NCAP (2018)

Song

Cao

Chou

2020

Sensors

View full text Add to dashboard Cite

In order to evaluate the effectiveness of a pedestrian-automatic emergency braking (PAEB) system on pedestrian protection, a set of PAEB test equipment was developed according to the test requirement of China-New Car Assessment Program (C-NCAP) (2018) in this study. In the aspect of system control strategy, global positioning system (GPS) differential positioning was used to achieve the required measurement and positioning accuracy, the collaborative control between the PAEB test equipment and automated driving robot (ADR) was achieved by wireless communication, and the motion state of the dummy target in the PAEB system was controlled by using the S-shaped-curve velocity control method. Part of the simulations and field tests were conducted according to the scenario requirements specified in C-NCAP (2018). The experimental and simulated results showed that the test equipment demonstrated high accuracy and precision in the process of testing, the dummy target movement was smooth and stable, complying with the requirements of PAEB tests set forth in C-NCAP (2018), and yielding satisfactory results as designed. Subsequently, the performance of the AEB of a vehicle under test (VUT) was conducted and the score for star-rating to evaluate the performance level of AEB calculated. Results indicated the developed test equipment in this study could be used to evaluate the performance of the PAEB system with effectiveness.

show abstract

“…The worst-case scenario evaluation methods check the influence of the IDSs on the occupant damage of the cases in the accident or near-miss incident database by simulation tools. Badea-Romero et al 8 and Dai 9 applied this method to evaluate auxiliary braking systems. This approach is similar to test matrix, with a prerequisite that the evaluated system does not affect the likelihood of an accident scene.…”

Section: Introductionmentioning

confidence: 99%

Safety benefit evaluation of intelligent driving systems based on multisource data mining

Chen

Luo

et al. 2019

Proceedings of the Institution of Mechanical Engineers, Part D:

View full text Add to dashboard Cite

Different manufacturers achieve intelligent driving system function diversely, which imposes higher impartiality requirements for the evaluation method of the third party. To this end, this article presents a safety benefit evaluation method of intelligent driving systems based on multi-source data mining. On the basis of the discussion over the nature of general system identification, this approach uses neural network to learn the behavior of the evaluated object using the running vehicle data collected and provided by the manufacturer. Combined with the trained network controller, the test scenario model and the car following model extracted from the field operational tests data, and with the occupant injury model obtained from the accident data, Monte Carlo random simulation is used to calculate the injury risk with or without the evaluated system, then the safety benefit by comparison is estimated. In this article, the adaptive cruise system and the automatic emergency braking system are evaluated. The results show that the neural network can accurately imitate the behavior of the object to be evaluated. There is only 0.01 error between the evaluation results using this network and the real object.

show abstract

Assessing the benefit of the brake assist system for pedestrian injury mitigation through real-world accident investigations

Cited by 14 publications

References 4 publications

Surrogate Safety Measures Prediction at Multiple Timescales in V2P Conflicts Based on Gated Recurrent Unit

Surrogate Safety Measures Prediction at Multiple Timescales in V2P Conflicts Based on Gated Recurrent Unit

Development of Test Equipment for Pedestrian-Automatic Emergency Braking Based on C-NCAP (2018)

Safety benefit evaluation of intelligent driving systems based on multisource data mining

Contact Info

Product

Resources

About