Detecting the adherence of driving rules in an energy-efficient, safe and adaptive driving system

Yay, Emre; Madrid, Natividad Martínez; Ramírez, Juan Antonio Ortega

doi:10.1016/j.eswa.2015.10.044

Cited by 3 publications

(4 citation statements)

References 17 publications

(15 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Our collected aggressive driving behaviors (ADBs) dataset 1 involves three different car trips that averaged 70 minutes. The tools and settings used for collecting the dataset were as follows: (I) The vehicle used was the Hyundai Elantra IV) The rate of sampling of the sensors used stood at 100 HZ; (V) The drivers involved in the data collection each had driving experience of 10 years; and finally, (VI) The roads on which the driving behaviors were tested were dry and had asphalt for pavement, and the weather in which the experiment took place was sunny.…”

Section: A Data Collectionmentioning

confidence: 99%

See 1 more Smart Citation

An Effective Bio-Signal-Based Driver Behavior Monitoring System Using a Generalized Deep Learning Approach

et al. 2020

View full text Add to dashboard Cite

Recent years have seen increasing utilization of deep learning methods to analyze large collections of medical data and signals effectively in the Internet of Medical Things (IoMT) environment.Application of these methods to medical signals and images can help caregivers form proper decisionmaking. One of the important IoMT medical application areas includes aggressive driving behaviors to mitigate road incidents and crashes. Various IoMT-enabled body sensors or camera sensors can be utilized for real-time monitoring and detection of drivers' bio-signal status such as heart rate, blood pressure, and drivers' behaviors. However, it requires a lightweight detection module and a powerful training module with realtime storing and analysis of drivers' behaviors data from these medical devices to detect driving behaviors and provides instant feedback by the administrator for safety, gas emissions, and energy/fuel consumption. Therefore, in this paper, we propose a bio-signal-based system for real-time detection of aggressive driving behaviors using a deep convolutional neural network (DCNN) model with edge and cloud technologies. More precisely, the system consists of three modules, which are the driving behaviors detection module implemented on edge devices in the vehicle, the training module implemented in the cloud platform, and the analyzing module placed in the monitoring environment connected with a telecommunication network. The DCNN model of the proposed system is evaluated using a holdout test set of 30% on two different processed bio-signal datasets. These two processed bio-signal datasets are generated from our collected bio-signal dataset by using two different time windows and two different time steps. The experimental results show that the proposed DCNN model achieves 73.02% of validation accuracy on the processed dataset 1 and 79.15% of validation accuracy on the processed dataset 2. The results confirm the appropriateness and applicability of the proposed deep learning model for detecting driving aggressive behaviors using bio-signal data.INDEX TERMS Internet of Medical Things (IoMT), bio-signal, aggressive driving behaviors, deep convolutional neural networks (DCNNs), cloud computing, Raspberry Pi.

show abstract

Section: A Data Collectionmentioning

confidence: 99%

“…It has been established that the behavior of drivers determines road safety to a great extent [1]. Moreover, many road accidents have been attributed to the driver's behavior…”

Section: Introductionmentioning

confidence: 99%

An Effective Bio-Signal-Based Driver Behavior Monitoring System Using a Generalized Deep Learning Approach

et al. 2020

View full text Add to dashboard Cite

show abstract

“…However, the use of local real-world driving cycles in optimisation models is important, since they can differ strongly from standardised driving cycles or from assuming an average value for energy use of buses. As a result, the real-world heterogeneity in transport systems is neglected (Millo et al 2014;Wang et al 2015;Xu et al 2015;Yay et al 2016;Zhang et al 2014).…”

Section: Literature Reviewmentioning

confidence: 99%

Comparison of management strategies for the charging schedule and all-electric operation of a plug-in hybrid-electric bi-articulated bus fleet

Dreier

Rudin²,

Howells

2020

Public Transp

View full text Add to dashboard Cite

This study developed a real-time optimisation (RTO) model that uses real-world bus operation data, i.e. route-specific and time-specific driving cycles. Potentials for energy savings and all-electric operation were estimated for a plug-in hybridelectric bi-articulated bus fleet (PLUG scenario) that can be managed according to different management strategies. Five strategies, A to E, were simulated that manage the charging schedule and all-electric operation with different priorities: PLUG-A, prioritise buses for charging by arrival times at the charging station (first come, first served); PLUG-B, prioritise buses for charging by energy intensities of the bus routes; PLUG-C, minimise the total energy use of the bus fleet; PLUG-D, maximise the total all-electric time of the bus fleet; and PLUG-E, maximise the total all-electric distance of the bus fleet. For comparison, a business-as-usual (BAU) scenario with conventional buses and another scenario with hybrid-electric buses (HYB) were simulated. Two weeks of real-world bus operation data from the city of Curitiba in Brazil were used as input data. The study finds that total energy savings of 17% and 27% in the HYB and PLUG scenarios can be achieved compared to the BAU scenario, respectively. Meanwhile, the average shares of the total all-electric time (TAET) and total all-electric distance (TAED) to the total bus fleet operation amount to 20% and 14% in the HYB scenario. Furthermore, both TAET and TAED in the PLUG scenario depend strongly on the chosen strategy amounting to ranges of 21-64% and 17-61%, respectively. Simultaneous maxima were found for strategy D.

show abstract

“…Due to the fact that standardised driving cycles can strongly differ from the characteristics of real-world operation, e.g. as found by (Zhang et al 2014;Wang et al 2015;Xu et al 2015;Yay et al 2016), local real-world driving cycles were used in the simulations. Additionally, the elevation profile was considered to include the slightly hilly topology of the BRT route (Fig.…”

Section: Driving Cycles and Elevation Profilementioning

confidence: 99%

The influence of passenger load, driving cycle, fuel price and different types of buses on the cost of transport service in the BRT system in Curitiba, Brazil

et al. 2018

View full text Add to dashboard Cite

This study analyses the influence of passenger load, driving cycle, fuel price and four different types of buses on the cost of transport service for one bus rapid transit (BRT) route in Curitiba, Brazil. First, the energy use is estimated for different passenger loads and driving cycles for a conventional bi-articulated bus (ConvBi), a hybrid-electric twoaxle bus (HybTw), a hybrid-electric articulated bus (HybAr) and a plug-in hybrid-electric two-axle bus (PlugTw). Then, the fuel cost and uncertainty are estimated considering the fuel price trends in the past. Based on this and additional cost data, replacement scenarios for the currently operated ConvBi fleet are determined using a techno-economic optimisation model. The lowest fuel cost ranges for the passenger load are estimated for PlugTw amounting to (0.198-0.289) USD/km, followed by (0.255-0.315) USD/km for HybTw, (0.298-0.375) USD/km for HybAr and (0.552-0.809) USD/km for ConvBi. In contrast, the coefficient of variation ( C v ) of the combined standard uncertainty is the highest for PlugTw ( C v : 15-17%) due to stronger sensitivity to varying bus driver behaviour, whereas it is the least for ConvBi ( C v : 8%). The scenario analysis shows that a complete replacement of the ConvBi fleet leads to considerable higher cost of transport service on the BRT route, amounting to an increase by 64% to 139%, depending on the bus fleet composition. Meanwhile, the service quality is improved resulting in 42% up to 64% less waiting time for passengers at a bus stop.

show abstract

Detecting the adherence of driving rules in an energy-efficient, safe and adaptive driving system

Cited by 3 publications

References 17 publications

An Effective Bio-Signal-Based Driver Behavior Monitoring System Using a Generalized Deep Learning Approach

An Effective Bio-Signal-Based Driver Behavior Monitoring System Using a Generalized Deep Learning Approach

Comparison of management strategies for the charging schedule and all-electric operation of a plug-in hybrid-electric bi-articulated bus fleet

The influence of passenger load, driving cycle, fuel price and different types of buses on the cost of transport service in the BRT system in Curitiba, Brazil

Contact Info

Product

Resources

About