Giulia Del Serrone, G. Cantisani, Riccardo Grilli, P. Peluso
Long uphill stretches of single-carriageway rural roads with one lane per travel direction may reduce the Level of Service (LoS), due to the decreased speed of heavy vehicles. In those circumstances, a slowdown of traffic, resulting in the formation of platoons, may be generated due to the difficulty of performing overtaking maneuvers safely. To solve this critical issue, an additional (climbing) lane for slow vehicles may be included in the road platform. This study aims to evaluate the effectiveness of such climbing lanes in a real case in Italy (National Road n. 4 “Via Salaria”—around 44+000 km). Using a microsimulation model implemented in VISSIM, the study analyzes speeds and travel times, delays, and queuing waiting times, comparing the Actual Scenario (AS) without climbing lanes, with two counterfactual scenarios: the first one (CS1) with three stretches of climbing lanes, and the second one (CS2), with just two stretches, in which the first two additional lanes of CS1 are merged together. The obtained results confirm the effectiveness of installing climbing lanes on road sections with the described characteristics, and the potential of microsimulation models also to carry out such kind of evaluations.
{"title":"Effectiveness of Climbing Lanes for Slow-Moving Vehicles When Riding Uphill: A Microsimulation Study","authors":"Giulia Del Serrone, G. Cantisani, Riccardo Grilli, P. Peluso","doi":"10.3390/vehicles5030041","DOIUrl":"https://doi.org/10.3390/vehicles5030041","url":null,"abstract":"Long uphill stretches of single-carriageway rural roads with one lane per travel direction may reduce the Level of Service (LoS), due to the decreased speed of heavy vehicles. In those circumstances, a slowdown of traffic, resulting in the formation of platoons, may be generated due to the difficulty of performing overtaking maneuvers safely. To solve this critical issue, an additional (climbing) lane for slow vehicles may be included in the road platform. This study aims to evaluate the effectiveness of such climbing lanes in a real case in Italy (National Road n. 4 “Via Salaria”—around 44+000 km). Using a microsimulation model implemented in VISSIM, the study analyzes speeds and travel times, delays, and queuing waiting times, comparing the Actual Scenario (AS) without climbing lanes, with two counterfactual scenarios: the first one (CS1) with three stretches of climbing lanes, and the second one (CS2), with just two stretches, in which the first two additional lanes of CS1 are merged together. The obtained results confirm the effectiveness of installing climbing lanes on road sections with the described characteristics, and the potential of microsimulation models also to carry out such kind of evaluations.","PeriodicalId":73282,"journal":{"name":"IEEE Intelligent Vehicles Symposium. IEEE Intelligent Vehicles Symposium","volume":"33 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89297585","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In today’s automotive industry, digital technology trends such as Big Data, Digital Twin, and Hardware-in-the-loop simulations using synthetic data offer opportunities that have the potential to transform the entire industry towards being more software-oriented and thus more effective and environmentally friendly. In this paper, we propose generative models to synthesize car features related to vehicle speed: brake pressure, percentage of the pressed throttle pedal, engaged gear, and engine RPM. Synthetic data are essential to digitize Hardware-in-the-loop integration testing of the vehicle’s dashboard, navigation, or infotainment and for Digital Twin simulations. We trained models based on Multilayer Perceptron and bidirectional Long-Short Term Memory neural network for each feature. These models were evaluated on a real-world dataset and demonstrated sufficient accuracy in predicting the desired features. Combining our current research with previous work on generating a speed profile for an arbitrary trip, where Open Street Map data and elevation data are available, allows us to digitally drive this trip. At the time of writing, we are unaware of any similar data-driven approach for generating desired speed-related features.
{"title":"Synthesizing Vehicle Speed-Related Features with Neural Networks","authors":"Michal Krepelka, J. Vraný","doi":"10.3390/vehicles5030040","DOIUrl":"https://doi.org/10.3390/vehicles5030040","url":null,"abstract":"In today’s automotive industry, digital technology trends such as Big Data, Digital Twin, and Hardware-in-the-loop simulations using synthetic data offer opportunities that have the potential to transform the entire industry towards being more software-oriented and thus more effective and environmentally friendly. In this paper, we propose generative models to synthesize car features related to vehicle speed: brake pressure, percentage of the pressed throttle pedal, engaged gear, and engine RPM. Synthetic data are essential to digitize Hardware-in-the-loop integration testing of the vehicle’s dashboard, navigation, or infotainment and for Digital Twin simulations. We trained models based on Multilayer Perceptron and bidirectional Long-Short Term Memory neural network for each feature. These models were evaluated on a real-world dataset and demonstrated sufficient accuracy in predicting the desired features. Combining our current research with previous work on generating a speed profile for an arbitrary trip, where Open Street Map data and elevation data are available, allows us to digitally drive this trip. At the time of writing, we are unaware of any similar data-driven approach for generating desired speed-related features.","PeriodicalId":73282,"journal":{"name":"IEEE Intelligent Vehicles Symposium. IEEE Intelligent Vehicles Symposium","volume":"415 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74126324","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Hexuan Li, V. Makkapati, Li Wan, E. Tomasch, H. Hoschopf, A. Eichberger
With the increasing complexity of automated driving features, it is crucial to adopt innovative approaches that combine hardware and software to validate prototype vehicles in the early stages of development. This article demonstrates the effectiveness of a Vehicle-in-the-Loop (ViL) testbed in conducting dynamic tests of vehicles equipped with highly automated driving functions. The tests are designed to replicate critical driving scenarios from real-world environments on the ViL testbed. In this study, the Apollo platform is utilized to develop an automated driving function that can perceive the surrounding traffic in a virtual environment and generate feasible trajectories. This is achieved with the help of a multibody simulation platform. The control commands from the simulated driving function are then transmitted to the real vehicle to execute the planned action. The results demonstrate that critical traffic scenarios can be replicated more safely and repeatedly on the ViL testbed. Meanwhile, the Apollo-based driving function can effectively and comfortably cope with critical scenarios. Importantly, this study marks a significant milestone for the Apollo platform as it is implemented in a real-time system and tested on a ViL testbed.
{"title":"Validation of Automated Driving Function Based on the Apollo Platform: A Milestone for Simulation with Vehicle-in-the-Loop Testbed","authors":"Hexuan Li, V. Makkapati, Li Wan, E. Tomasch, H. Hoschopf, A. Eichberger","doi":"10.3390/vehicles5020039","DOIUrl":"https://doi.org/10.3390/vehicles5020039","url":null,"abstract":"With the increasing complexity of automated driving features, it is crucial to adopt innovative approaches that combine hardware and software to validate prototype vehicles in the early stages of development. This article demonstrates the effectiveness of a Vehicle-in-the-Loop (ViL) testbed in conducting dynamic tests of vehicles equipped with highly automated driving functions. The tests are designed to replicate critical driving scenarios from real-world environments on the ViL testbed. In this study, the Apollo platform is utilized to develop an automated driving function that can perceive the surrounding traffic in a virtual environment and generate feasible trajectories. This is achieved with the help of a multibody simulation platform. The control commands from the simulated driving function are then transmitted to the real vehicle to execute the planned action. The results demonstrate that critical traffic scenarios can be replicated more safely and repeatedly on the ViL testbed. Meanwhile, the Apollo-based driving function can effectively and comfortably cope with critical scenarios. Importantly, this study marks a significant milestone for the Apollo platform as it is implemented in a real-time system and tested on a ViL testbed.","PeriodicalId":73282,"journal":{"name":"IEEE Intelligent Vehicles Symposium. IEEE Intelligent Vehicles Symposium","volume":"30 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82560302","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Marcos Moreno-Gonzalez, Antonio Artuñedo, J. Villagrá, C. Join, M. Fliess
One of the challenges of autonomous driving is to increase the number of situations in which an intelligent vehicle can continue to operate without human intervention. This requires path-tracking control to keep the vehicle stable while following the road, regardless of the shape of the road or the longitudinal speed at which it is moving. In this work, a control strategy framed in the Model-Free Control paradigm is presented to control the lateral vehicle dynamics in a decoupled control architecture. This strategy is designed to guide the vehicle through trajectories with diverse dynamic constraints and over a wide speed range. A design method for this control strategy is proposed, and metrics for trajectory tracking quality, system stability, and passenger comfort are applied to evaluate the controller’s performance. Finally, simulation and real-world tests show that the developed strategy is able to track realistic trajectories with a high degree of accuracy, safety, and comfort.
{"title":"Speed-Adaptive Model-Free Path-Tracking Control for Autonomous Vehicles: Analysis and Design","authors":"Marcos Moreno-Gonzalez, Antonio Artuñedo, J. Villagrá, C. Join, M. Fliess","doi":"10.3390/vehicles5020038","DOIUrl":"https://doi.org/10.3390/vehicles5020038","url":null,"abstract":"One of the challenges of autonomous driving is to increase the number of situations in which an intelligent vehicle can continue to operate without human intervention. This requires path-tracking control to keep the vehicle stable while following the road, regardless of the shape of the road or the longitudinal speed at which it is moving. In this work, a control strategy framed in the Model-Free Control paradigm is presented to control the lateral vehicle dynamics in a decoupled control architecture. This strategy is designed to guide the vehicle through trajectories with diverse dynamic constraints and over a wide speed range. A design method for this control strategy is proposed, and metrics for trajectory tracking quality, system stability, and passenger comfort are applied to evaluate the controller’s performance. Finally, simulation and real-world tests show that the developed strategy is able to track realistic trajectories with a high degree of accuracy, safety, and comfort.","PeriodicalId":73282,"journal":{"name":"IEEE Intelligent Vehicles Symposium. IEEE Intelligent Vehicles Symposium","volume":"15 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81662754","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Fire is a stable exothermic chain reaction of flammable materials brought together with oxygen or other oxidizing substances under certain conditions, occurring uncontrollably. Fire vehicles interfere with many types of fire, such as wildfires, factory fires, building fires, etc. During this intervention, fire vehicles generally use water or foam. In this study, new effective fire suppression applications are investigated. Thermal camera applications in fire trucks and also new extinguishing agents—boron-based chemicals—were tested in forest fire simulations. In these experiments, it was observed that the thermal camera detected the fire as soon as it occurred. It seemed appropriate to use thermal cameras for all types of fire vehicles (foam trucks, water tankers, rescue trucks, etc.). It was seen that the thermal camera application could detect and monitor the fire during the fire-extinguishing work of the firefighters. The boron-based fire suppressant had a better extinguishing and cooling effect than water in the experiments. Compared to the water used as a traditional method, the liquid boron-based extinguisher provided 22% faster—while the solid boron-based extinguisher provided 42% faster—suppression and cooling. With three separate experiments, it is predicted that thermal camera applications and the use of boron-based extinguishers in fire vehicles can lead to an effective and positive transformation in the coming years.
{"title":"Modernization of Fire Vehicles with New Technologies and Chemicals","authors":"C. Un, K. Aydın","doi":"10.3390/vehicles5020037","DOIUrl":"https://doi.org/10.3390/vehicles5020037","url":null,"abstract":"Fire is a stable exothermic chain reaction of flammable materials brought together with oxygen or other oxidizing substances under certain conditions, occurring uncontrollably. Fire vehicles interfere with many types of fire, such as wildfires, factory fires, building fires, etc. During this intervention, fire vehicles generally use water or foam. In this study, new effective fire suppression applications are investigated. Thermal camera applications in fire trucks and also new extinguishing agents—boron-based chemicals—were tested in forest fire simulations. In these experiments, it was observed that the thermal camera detected the fire as soon as it occurred. It seemed appropriate to use thermal cameras for all types of fire vehicles (foam trucks, water tankers, rescue trucks, etc.). It was seen that the thermal camera application could detect and monitor the fire during the fire-extinguishing work of the firefighters. The boron-based fire suppressant had a better extinguishing and cooling effect than water in the experiments. Compared to the water used as a traditional method, the liquid boron-based extinguisher provided 22% faster—while the solid boron-based extinguisher provided 42% faster—suppression and cooling. With three separate experiments, it is predicted that thermal camera applications and the use of boron-based extinguishers in fire vehicles can lead to an effective and positive transformation in the coming years.","PeriodicalId":73282,"journal":{"name":"IEEE Intelligent Vehicles Symposium. IEEE Intelligent Vehicles Symposium","volume":"2 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86017521","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This paper attempts to develop a Dynamic Mode Decomposition (DMD)-based Reduced Order Model (ROMs) that can quickly but accurately predict the forces and moments experienced by a road vehicle such that they be used by an on-board controller to determine the vehicle’s trajectory. DMD can linearize a large dataset of high-dimensional measurements by decomposing them into low-dimensional coherent structures and associated time dynamics. This ROM can then also be applied to predict the future state of the fluid flow. Existing literature on DMD is limited to low Reynolds number applications. This paper presents DMD analyses of the flow around an idealized road vehicle, called the Ahmed body, at a Reynolds number of 2.7×106. The high-dimensional dataset used in this paper was collected from a computational fluid dynamics (CFD) simulation performed using the Menter’s Shear Stress Transport (SST) turbulence model within the context of Improved Delayed Detached Eddy Simulations (IDDES). The DMD algorithm, as available in the literature, was found to suffer nonphysical dampening of the medium-to-high frequency modes. Enhancements to the existing algorithm were explored, and a modified DMD approach is presented in this paper, which includes: (a) a requirement of higher sampling rate to obtain a higher resolution of data, and (b) a custom filtration process to remove spurious modes. The modified DMD algorithm thus developed was applied to the high-Reynolds-number, separation-dominated flow past the idealized ground vehicle. The effectiveness of the modified algorithm was tested by comparing future predictions of force and moment coefficients as predicted by the DMD-based ROM to the reference CFD simulation data, and they were found to offer significant improvement.
{"title":"Application of the DMD Approach to High-Reynolds-Number Flow over an Idealized Ground Vehicle","authors":"Adit Misar, N. Tison, V. Korivi, M. Uddin","doi":"10.3390/vehicles5020036","DOIUrl":"https://doi.org/10.3390/vehicles5020036","url":null,"abstract":"This paper attempts to develop a Dynamic Mode Decomposition (DMD)-based Reduced Order Model (ROMs) that can quickly but accurately predict the forces and moments experienced by a road vehicle such that they be used by an on-board controller to determine the vehicle’s trajectory. DMD can linearize a large dataset of high-dimensional measurements by decomposing them into low-dimensional coherent structures and associated time dynamics. This ROM can then also be applied to predict the future state of the fluid flow. Existing literature on DMD is limited to low Reynolds number applications. This paper presents DMD analyses of the flow around an idealized road vehicle, called the Ahmed body, at a Reynolds number of 2.7×106. The high-dimensional dataset used in this paper was collected from a computational fluid dynamics (CFD) simulation performed using the Menter’s Shear Stress Transport (SST) turbulence model within the context of Improved Delayed Detached Eddy Simulations (IDDES). The DMD algorithm, as available in the literature, was found to suffer nonphysical dampening of the medium-to-high frequency modes. Enhancements to the existing algorithm were explored, and a modified DMD approach is presented in this paper, which includes: (a) a requirement of higher sampling rate to obtain a higher resolution of data, and (b) a custom filtration process to remove spurious modes. The modified DMD algorithm thus developed was applied to the high-Reynolds-number, separation-dominated flow past the idealized ground vehicle. The effectiveness of the modified algorithm was tested by comparing future predictions of force and moment coefficients as predicted by the DMD-based ROM to the reference CFD simulation data, and they were found to offer significant improvement.","PeriodicalId":73282,"journal":{"name":"IEEE Intelligent Vehicles Symposium. IEEE Intelligent Vehicles Symposium","volume":"14 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90381184","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Gereon Kortenbruck, Lukas Jakubczyk, Daniel Frank Nowak
Nowadays, cars are an essential part of daily life, and failures, especially of the engine, need to be avoided. Here, we used the determination of the battery voltage as a reference measurement to determine possible malfunctions. Thereby, we compared the use of a digital oscilloscope with the direct measurement of the battery voltage via the electronic control unit. The two devices were evaluated based on criteria such as price, sampling rate, parallel measurements, simplicity, and technical understanding required. Results showed that the oscilloscope (Picoscope 3204D MSO) is more suitable for complex measurements due to its higher sampling rate, accuracy, and versatility. The on-board diagnostics (VCDS HEX-V2) is more accessible to non-professionals, but it is limited in its capabilities. We found that the use of an oscilloscope, specifically the Picoscope, is preferable to measure battery voltage during the engine start-up process, as it provides more accurate and reliable results. However, further investigation is required to analyse numerous influences on the cranking process and the final decision for the appropriate measurement device is case specific.
{"title":"Voltage Signals Measured Directly at the Battery and via On-Board Diagnostics: A Comparison","authors":"Gereon Kortenbruck, Lukas Jakubczyk, Daniel Frank Nowak","doi":"10.3390/vehicles5020035","DOIUrl":"https://doi.org/10.3390/vehicles5020035","url":null,"abstract":"Nowadays, cars are an essential part of daily life, and failures, especially of the engine, need to be avoided. Here, we used the determination of the battery voltage as a reference measurement to determine possible malfunctions. Thereby, we compared the use of a digital oscilloscope with the direct measurement of the battery voltage via the electronic control unit. The two devices were evaluated based on criteria such as price, sampling rate, parallel measurements, simplicity, and technical understanding required. Results showed that the oscilloscope (Picoscope 3204D MSO) is more suitable for complex measurements due to its higher sampling rate, accuracy, and versatility. The on-board diagnostics (VCDS HEX-V2) is more accessible to non-professionals, but it is limited in its capabilities. We found that the use of an oscilloscope, specifically the Picoscope, is preferable to measure battery voltage during the engine start-up process, as it provides more accurate and reliable results. However, further investigation is required to analyse numerous influences on the cranking process and the final decision for the appropriate measurement device is case specific.","PeriodicalId":73282,"journal":{"name":"IEEE Intelligent Vehicles Symposium. IEEE Intelligent Vehicles Symposium","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135693018","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The Stanley controller is a proven approach for path tracking control in automated vehicles. If time delays occur, for example, in signal processing and steering angle control, precision and stability decrease. In this article, enhancements for the Stanley controller are proposed to achieve stable behavior with improved tracking accuracy. The approach uses the curvature of the path as feedforward, whereby the reference point for the feedforward input differs from that of the controller setpoints. By choosing a point further along the path, the negative effects of system delay are reduced. First, the parameters of the Stanley controller are calibrated using a straight line and circle maneuver. Then, the newly introduced feedforward parameter is optimized on a dynamic circuit. The approach was evaluated in simulation and validated on a demonstrator vehicle. The validation tests with the demonstrator vehicle on the dynamic circuit revealed a reduction of the root-mean-square cross-track error from 0.11 m to 0.03 m compared to the Stanley controller. We proved that the proposed approach optimizes the Stanley controller in terms of compensating for the negative effects of system delay. This allows it to be used in a wider range of applications that would otherwise require a more complex control approach.
{"title":"Pragmatic and Effective Enhancements for Stanley Path-Tracking Controller by Considering System Delay","authors":"Alexander Seiffer, Michael Frey, F. Gauterin","doi":"10.3390/vehicles5020034","DOIUrl":"https://doi.org/10.3390/vehicles5020034","url":null,"abstract":"The Stanley controller is a proven approach for path tracking control in automated vehicles. If time delays occur, for example, in signal processing and steering angle control, precision and stability decrease. In this article, enhancements for the Stanley controller are proposed to achieve stable behavior with improved tracking accuracy. The approach uses the curvature of the path as feedforward, whereby the reference point for the feedforward input differs from that of the controller setpoints. By choosing a point further along the path, the negative effects of system delay are reduced. First, the parameters of the Stanley controller are calibrated using a straight line and circle maneuver. Then, the newly introduced feedforward parameter is optimized on a dynamic circuit. The approach was evaluated in simulation and validated on a demonstrator vehicle. The validation tests with the demonstrator vehicle on the dynamic circuit revealed a reduction of the root-mean-square cross-track error from 0.11 m to 0.03 m compared to the Stanley controller. We proved that the proposed approach optimizes the Stanley controller in terms of compensating for the negative effects of system delay. This allows it to be used in a wider range of applications that would otherwise require a more complex control approach.","PeriodicalId":73282,"journal":{"name":"IEEE Intelligent Vehicles Symposium. IEEE Intelligent Vehicles Symposium","volume":"10 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88480836","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In this paper, we present a feasibility study on predicting the torque signal of a passenger car with the help of a neural network. In addition, we analyze the possibility of using the proposed model structure for temperature prediction. This was carried out with a neural network, specifically a three-layer long short-term memory (LSTM) network. The data used were real road load data from a Jaguar Land Rover Evoque with a Twinster gearbox from GKN. The torque prediction generated good results with an accuracy of 55% and a root mean squared error (RMSE) of 49 Nm, considering that the data were not generated under laboratory conditions. However, the performance of predicting the temperature signal was not satisfying with a coefficient of determination (R2) score of −1.396 and an RMSE score of 69.4 °C. The prediction of the torque signal with the three-layer LSTM network was successful but the transferability of the network to another signal (temperature) was not proven. The knowledge gained from this investigation can be of importance for the development of virtual sensor technology.
{"title":"Feasibility Study of Wheel Torque Prediction with a Recurrent Neural Network Using Vehicle Data","authors":"Miriam Weinkath, Simon Nett, Chong Dae Kim","doi":"10.3390/vehicles5020033","DOIUrl":"https://doi.org/10.3390/vehicles5020033","url":null,"abstract":"In this paper, we present a feasibility study on predicting the torque signal of a passenger car with the help of a neural network. In addition, we analyze the possibility of using the proposed model structure for temperature prediction. This was carried out with a neural network, specifically a three-layer long short-term memory (LSTM) network. The data used were real road load data from a Jaguar Land Rover Evoque with a Twinster gearbox from GKN. The torque prediction generated good results with an accuracy of 55% and a root mean squared error (RMSE) of 49 Nm, considering that the data were not generated under laboratory conditions. However, the performance of predicting the temperature signal was not satisfying with a coefficient of determination (R2) score of −1.396 and an RMSE score of 69.4 °C. The prediction of the torque signal with the three-layer LSTM network was successful but the transferability of the network to another signal (temperature) was not proven. The knowledge gained from this investigation can be of importance for the development of virtual sensor technology.","PeriodicalId":73282,"journal":{"name":"IEEE Intelligent Vehicles Symposium. IEEE Intelligent Vehicles Symposium","volume":"9 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-05-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83119165","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
E. Tomanik, Antonio J. Jiménez-Reyes, Victor Tomanik, B. Tormos
Transient car emission tests generate huge amount of test data, but their results are usually evaluated only using their “accumulated” cycle values according to the homologation limits. In this work, two machine learning models were developed and applied to a truck RDE test and two light-duty vehicle chassis emission tests. Different from the conventional approach, the engine parameters and fuel consumption were acquired from the Engine Control Unit, not from the test measurement equipment. Instantaneous engine values were used as input in machine-learning-based digital twins. This novel approach allows for much less costly vehicle tests and optimizations. The paper’s novel approach and developed digital twins model were able to predict both instantaneous and accumulated fuel consumption with good accuracy, and also for tests cycles different to the one used to train the model.
{"title":"Machine-Learning-Based Digital Twins for Transient Vehicle Cycles and Their Potential for Predicting Fuel Consumption","authors":"E. Tomanik, Antonio J. Jiménez-Reyes, Victor Tomanik, B. Tormos","doi":"10.3390/vehicles5020032","DOIUrl":"https://doi.org/10.3390/vehicles5020032","url":null,"abstract":"Transient car emission tests generate huge amount of test data, but their results are usually evaluated only using their “accumulated” cycle values according to the homologation limits. In this work, two machine learning models were developed and applied to a truck RDE test and two light-duty vehicle chassis emission tests. Different from the conventional approach, the engine parameters and fuel consumption were acquired from the Engine Control Unit, not from the test measurement equipment. Instantaneous engine values were used as input in machine-learning-based digital twins. This novel approach allows for much less costly vehicle tests and optimizations. The paper’s novel approach and developed digital twins model were able to predict both instantaneous and accumulated fuel consumption with good accuracy, and also for tests cycles different to the one used to train the model.","PeriodicalId":73282,"journal":{"name":"IEEE Intelligent Vehicles Symposium. IEEE Intelligent Vehicles Symposium","volume":"107 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84596123","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：