Investigation of Bus Drivers’ Reaction to ADAS Warning System: Application of the Gaussian Mixed Model

Ye, Wei; Xu, Yueru; Zhou, Feixiang; Shi, Xiaomeng; Ye, Zhirui

doi:10.3390/su13168759

Cited by 8 publications

(3 citation statements)

References 41 publications

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“…Although there are attempts to make buses autonomous [9], advanced driver-assistance systems [10] can operate where full autonomy is not permitted in public transportation. Unfortunately, published works on ADAS for city buses most often focus on solutions to collision warning or lane departure [11], leaving out the difficult task of docking electric buses to chargers. While an ADAS solution can support the driver in a number of different functions, such as cruise control [12], recognition of traffic lights [13], or even detection of pedestrians [14], the system we consider is aimed exclusively at facilitating the docking manoeuvres to charging stations as required by the business partners.…”

Section: Related Workmentioning

confidence: 99%

GNSS-Based Driver Assistance for Charging Electric City Buses: Implementation and Lessons Learned from Field Testing

Esfandiyar,

Ćwian,

Nowicki

et al. 2023

Remote Sensing

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Modern public transportation in urban areas increasingly relies on high-capacity buses. At the same time, the share of electric vehicles is increasing to meet environmental standards. This introduces problems when charging these vehicles from chargers at bus stops, as untrained drivers often find it difficult to execute docking manoeuvres on the charger. A practical solution to this problem requires a suitable advanced driver-assistance system (ADAS), which is a system used to automatise and make safer some of the tasks involved in driving a vehicle. In the considered case, ADAS supports docking to the electric charging station, and thus, it must solve two issues: precise positioning of the bus relative to the charger and motion planning in a constrained space. This paper addresses these issues by employing GNSS-based positioning and optimisation-based planning, resulting in an affordable solution to the ADAS for the docking of electric buses while recharging. We focus on the practical side of the system, showing how the necessary features were attained at a limited hardware and installation cost, also demonstrating an extensive evaluation of the fielded ADAS for an operator of public transportation in the city of Poznań in Poland.

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Section: Related Workmentioning

confidence: 99%

GNSS-Based Driver Assistance for Charging Electric City Buses: Implementation and Lessons Learned from Field Testing

Esfandiyar,

Ćwian,

Nowicki

et al. 2023

Remote Sensing

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“…Indeed, research shows that most traffic accidents are associated with unsafe driving behaviors [13,29]. Although many studies have attempted to evaluate risk-driving behavior [30,31], there is a notable shortage of research that has investigated the factors underlying individual driving behavior and quantifying the driving behavior patterns of professional drivers via long-term naturalistic driving data. The quantitative estimation of driving performance and behaviors using VBT is likely to be helpful in measuring a driver's risky driving behavior to decrease traffic accidents.…”

Section: Role Of Nds and Vbt In Addressing Human Factors-related Safe...mentioning

confidence: 99%

Optimal Duration of In-Vehicle Data Recorder Monitoring to Assess Bus Driver Behavior

Shichrur,

Ratzon

2023

Sensors

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This study examined the optimal sampling durations for in-vehicle data recorder (IVDR) data analysis, focusing on professional bus drivers. Vision-based technology (VBT) from Mobileye Inc. is an emerging technology for monitoring driver behavior and enhancing safety in advanced driver assistance systems (ADASs) and autonomous driving. VBT detects hazardous driving events by assessing distances to vehicles. This naturalistic study of 77 male bus drivers aimed to determine the optimal duration for monitoring professional bus driving patterns and the stabilization point in risky driving events over time using VBT and G-sensor-equipped buses. Of the initial cohort, 61 drivers’ VBT data and 66 drivers’ G-sensor data were suitable for analysis. Findings indicated that achieving a stable driving pattern required approximately 130 h of VBT data and 170 h of G-sensor data with an expected 10% error rate. Deviating downward from these durations led to higher error rates or unreliable data. The study found that VBT and G-sensor data are both valuable tools for driving assessment. Moreover, it underscored the effective application of VBT technology in driving behavior analysis as a way of assessing interventions and refining autonomous vehicle algorithms. These results provide practical recommendations for IVDR researchers, stressing the importance of adequate monitoring durations for reliable and accurate outcomes.

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“…Among the six studied technologies, Automatic Emergency Braking is the most impactful for road safety, providing effective accident reduction in three out of the four most frequent accident types. Wei Ye et al [13] developed a Gaussian mixed model to investigate bus drivers' reaction to ADAS warning system. In this research, drivers' reaction to lane departure warning and forward collision warning were investigated using 20 days of naturalistic driving data.…”

Section: Introductionmentioning

confidence: 99%

Identification of risky locations based on the CAS device-based alerts generated from moving vehicles: A Case study

Praneetha Devi,

Advani,

Sahitya

2024

IOP Conf. Ser.: Earth Environ. Sci.

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Increase in traffic accidents is one of the major concerns that affects economy of the country. To reduce traffic crashes, an advanced artificial intelligence technology for road safety known as ADAS (Advanced Driver Assistance System) is being adopted. Advanced driver assistance system is one of the groups of electronic technologies that assist drivers in driving and parking functions by giving an alert to drivers for safer decisions. The present study attempts to apply the CAS (Collision Avoidance System) in the city buses of Telangana state. The data generated from CAS device includes alert type like LDW (Lane Diversion Warning), HMW (Headway Monitoring Warning), FCW (Forward Collision Warning), PCW (Pedestrian Collision Warning) with location, date, time of the generated alert, and speed. The study uses the Geographical Information System-based platform for advanced level analysis. Total 190 km long road network is being used in this study. Alert data have been analysed to identify the risky locations and compared with the existing blackspots. These blackspots have been identified based on traditional methods of using FIR records. Results shows that higher number of alerts are generated at Kukatpally metro station. After obtaining unsafe locations through CAS analysis, the locations are then validated with existing blackspot locations.

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Investigation of Bus Drivers’ Reaction to ADAS Warning System: Application of the Gaussian Mixed Model

Cited by 8 publications

References 41 publications

GNSS-Based Driver Assistance for Charging Electric City Buses: Implementation and Lessons Learned from Field Testing

GNSS-Based Driver Assistance for Charging Electric City Buses: Implementation and Lessons Learned from Field Testing

Optimal Duration of In-Vehicle Data Recorder Monitoring to Assess Bus Driver Behavior

Identification of risky locations based on the CAS device-based alerts generated from moving vehicles: A Case study

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