Pub Date : 2022-06-05DOI: 10.1109/iv51971.2022.9827305
Tobias Moers, Lennart Vater, R. Krajewski, Julian Bock, A. Zlocki, L. Eckstein
Development and safety validation of highly automated vehicles increasingly relies on data and data-driven methods. In processing sensor datasets for environment perception, it is common to use public and commercial datasets for training and evaluating machine learning based systems. For system-level evaluation and safety validation of an automated driving system, real-world trajectory datasets are of great value for several tasks in the process, i.a. for testing in simulation, scenario extraction or training of road user agent models. Ground-based recording methods such as sensor-equipped vehicles or infrastructure sensors are sometimes limited, for instance, due to their field of view. Camera-equipped drones, however, offer the ability to record road users without vehicle-to-vehicle occlusion and without influencing traffic. The highway drone dataset (highD) has shown that the recording method is efficient in terms of cumulative kilometers and has become a benchmark dataset for many research questions. It contains many vehicle interactions due to dense traffic, but lacks merging scenarios, which are challenging for highly automated vehicles. Therefore, we propose this highway drone dataset called exiD, recorded using camera-equipped drones at entries and exits on the German Autobahn. The dataset contains 69 172 road users classified as car, truck and vans and a total amount of more than 16 hours of measurement data. For non-commercial public research, the exiD dataset is available free of charge at https://www.exid-dataset.com.
{"title":"The exiD Dataset: A Real-World Trajectory Dataset of Highly Interactive Highway Scenarios in Germany","authors":"Tobias Moers, Lennart Vater, R. Krajewski, Julian Bock, A. Zlocki, L. Eckstein","doi":"10.1109/iv51971.2022.9827305","DOIUrl":"https://doi.org/10.1109/iv51971.2022.9827305","url":null,"abstract":"Development and safety validation of highly automated vehicles increasingly relies on data and data-driven methods. In processing sensor datasets for environment perception, it is common to use public and commercial datasets for training and evaluating machine learning based systems. For system-level evaluation and safety validation of an automated driving system, real-world trajectory datasets are of great value for several tasks in the process, i.a. for testing in simulation, scenario extraction or training of road user agent models. Ground-based recording methods such as sensor-equipped vehicles or infrastructure sensors are sometimes limited, for instance, due to their field of view. Camera-equipped drones, however, offer the ability to record road users without vehicle-to-vehicle occlusion and without influencing traffic. The highway drone dataset (highD) has shown that the recording method is efficient in terms of cumulative kilometers and has become a benchmark dataset for many research questions. It contains many vehicle interactions due to dense traffic, but lacks merging scenarios, which are challenging for highly automated vehicles. Therefore, we propose this highway drone dataset called exiD, recorded using camera-equipped drones at entries and exits on the German Autobahn. The dataset contains 69 172 road users classified as car, truck and vans and a total amount of more than 16 hours of measurement data. For non-commercial public research, the exiD dataset is available free of charge at https://www.exid-dataset.com.","PeriodicalId":184622,"journal":{"name":"2022 IEEE Intelligent Vehicles Symposium (IV)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130883785","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}
Pub Date : 2022-06-05DOI: 10.1109/iv51971.2022.9827348
E. Kargar, V. Kyrki
Driving in a complex and dynamic urban environment is a difficult task that requires a complex decision policy. In order to make informed decisions, one needs to gain an understanding of the long-range context and the importance of other vehicles. In this work, we propose to use Vision Transformer (ViT) to learn a driving policy in urban settings with birds-eye-view (BEV) input images. The ViT network learns the global context of the scene more effectively than with earlier proposed Convolutional Neural Networks (ConvNets). Furthermore, ViT’s attention mechanism helps to learn an attention map for the scene which allows the ego car to determine which surrounding cars are important to its next decision. We demonstrate that a DQN agent with a ViT backbone outperforms baseline algorithms with ConvNet backbones pre-trained in various ways. In particular, the proposed method helps reinforcement learning algorithms to learn faster, with increased performance and less data than baselines.
{"title":"Vision Transformer for Learning Driving Policies in Complex and Dynamic Environments","authors":"E. Kargar, V. Kyrki","doi":"10.1109/iv51971.2022.9827348","DOIUrl":"https://doi.org/10.1109/iv51971.2022.9827348","url":null,"abstract":"Driving in a complex and dynamic urban environment is a difficult task that requires a complex decision policy. In order to make informed decisions, one needs to gain an understanding of the long-range context and the importance of other vehicles. In this work, we propose to use Vision Transformer (ViT) to learn a driving policy in urban settings with birds-eye-view (BEV) input images. The ViT network learns the global context of the scene more effectively than with earlier proposed Convolutional Neural Networks (ConvNets). Furthermore, ViT’s attention mechanism helps to learn an attention map for the scene which allows the ego car to determine which surrounding cars are important to its next decision. We demonstrate that a DQN agent with a ViT backbone outperforms baseline algorithms with ConvNet backbones pre-trained in various ways. In particular, the proposed method helps reinforcement learning algorithms to learn faster, with increased performance and less data than baselines.","PeriodicalId":184622,"journal":{"name":"2022 IEEE Intelligent Vehicles Symposium (IV)","volume":"96 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130967195","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}
Pub Date : 2022-06-05DOI: 10.1109/iv51971.2022.9827149
Muhammad Faris, P. Falcone, J. Sjöberg
This paper considers a coordination problem for Connected and Automated Vehicles (CAVs) in mixed traffic at unsignalized intersections. In such a setting, the behavior of the Human-Driven Vehicles (HDVs) is difficult to predict, thus challenging the formulation and the solution of the coordination problem. To solve this problem, we propose a coordination strategy, where CAVs are used as both sensors and actuators in mixed platoons. A timeslot-based approach is used to coordinate the occupancy of the intersection and to compensate for the HDVs behavior. The proposed approach has a bi-level optimization structure built upon the Model Predictive Control (MPC) framework that decides the crossing order and computes the vehicles’ commands. In simulations, we show that the choice of the HDV prediction model heavily affects the coordination by evaluating the performance of two different HDV models: car-following and constant velocity, where the latter demonstrates more consistent results in the presence of deviation of the HDVs’ behavior from a nominal model.
{"title":"Optimization-Based Coordination of Mixed Traffic at Unsignalized Intersections Based on Platooning Strategy","authors":"Muhammad Faris, P. Falcone, J. Sjöberg","doi":"10.1109/iv51971.2022.9827149","DOIUrl":"https://doi.org/10.1109/iv51971.2022.9827149","url":null,"abstract":"This paper considers a coordination problem for Connected and Automated Vehicles (CAVs) in mixed traffic at unsignalized intersections. In such a setting, the behavior of the Human-Driven Vehicles (HDVs) is difficult to predict, thus challenging the formulation and the solution of the coordination problem. To solve this problem, we propose a coordination strategy, where CAVs are used as both sensors and actuators in mixed platoons. A timeslot-based approach is used to coordinate the occupancy of the intersection and to compensate for the HDVs behavior. The proposed approach has a bi-level optimization structure built upon the Model Predictive Control (MPC) framework that decides the crossing order and computes the vehicles’ commands. In simulations, we show that the choice of the HDV prediction model heavily affects the coordination by evaluating the performance of two different HDV models: car-following and constant velocity, where the latter demonstrates more consistent results in the presence of deviation of the HDVs’ behavior from a nominal model.","PeriodicalId":184622,"journal":{"name":"2022 IEEE Intelligent Vehicles Symposium (IV)","volume":"49 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129693411","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}
Pub Date : 2022-06-05DOI: 10.1109/iv51971.2022.9827459
Vivian Lotz, Eva-Maria Schomakers, M. Ziefle
With the advent of automation in road traffic, vehicle interaction design is undergoing a major shift and facing new challenges. In this paper, we adopted a user-centered design approach to identify suitable interface types for the interaction between automated light vehicles for urban last-mile deliveries and their human operator. In an exploratory co-creation workshop, we first identified possible interface types with laypeople and logistics employees (N=8). Based on the workshop insights, we surveyed user acceptance of various interface options (e.g., app, voice, and gesture control), the situation- and user-dependency of interface acceptance, and the users’ motivations for preferring specific interface types (online survey study: N=188). The analysis revealed that ease of use, road safety, and compatibility were the most mentioned reasons for preferring a particular interface type. Additionally, results showed that app and voice control were, on average, perceived as most desirable. However, none of the queried interface types was assessed as a perfect fit for each interaction situation and user.
{"title":"Users’ Preferences for the Communication with Autonomous Micro-Vehicles","authors":"Vivian Lotz, Eva-Maria Schomakers, M. Ziefle","doi":"10.1109/iv51971.2022.9827459","DOIUrl":"https://doi.org/10.1109/iv51971.2022.9827459","url":null,"abstract":"With the advent of automation in road traffic, vehicle interaction design is undergoing a major shift and facing new challenges. In this paper, we adopted a user-centered design approach to identify suitable interface types for the interaction between automated light vehicles for urban last-mile deliveries and their human operator. In an exploratory co-creation workshop, we first identified possible interface types with laypeople and logistics employees (N=8). Based on the workshop insights, we surveyed user acceptance of various interface options (e.g., app, voice, and gesture control), the situation- and user-dependency of interface acceptance, and the users’ motivations for preferring specific interface types (online survey study: N=188). The analysis revealed that ease of use, road safety, and compatibility were the most mentioned reasons for preferring a particular interface type. Additionally, results showed that app and voice control were, on average, perceived as most desirable. However, none of the queried interface types was assessed as a perfect fit for each interaction situation and user.","PeriodicalId":184622,"journal":{"name":"2022 IEEE Intelligent Vehicles Symposium (IV)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131351934","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}
Pub Date : 2022-06-05DOI: 10.1109/iv51971.2022.9827231
Abhilash Y. Naik, Ariyan Bighashdel, P. Jancura, Gijs Dubbelman
For safe and comfortable navigation of autonomous vehicles, it is crucial to know the pedestrian’s intention of crossing the street. Generally, human drivers are aware of the traffic objects (e.g., crosswalks and traffic lights) in the environment while driving; likewise, these objects would play a crucial role for autonomous vehicles. In this research, we propose a novel pedestrian intention estimation method that not only takes into account the influence of traffic objects but also learns their contribution levels on the intention of the pedestrian. Our proposed method, referred to as Scene SpatioTemporal Graph Convolutional Network (Scene-STGCN), takes benefits from the strength of Graph Convolutional Networks and efficiently encodes the relationships between the pedestrian and the scene objects both spatially and temporally. We conduct several experiments on the Pedestrian Intention Estimation (PIE) dataset and illustrate the importance of scene objects and their contribution levels in the task of pedestrian intention estimation. Furthermore, we perform statistical analysis on the relevance of different traffic objects in the PIE dataset and carry out an ablation study on the effect of various information sources in the scene. Finally, we demonstrate the significance of the proposed Scene-STGCN through experimental comparisons with several baselines. The results indicate that our proposed Scene-STGCN outperforms the current state-of-the-art method by 0.03 in terms of ROC-AUC metric.
{"title":"Scene Spatio-Temporal Graph Convolutional Network for Pedestrian Intention Estimation","authors":"Abhilash Y. Naik, Ariyan Bighashdel, P. Jancura, Gijs Dubbelman","doi":"10.1109/iv51971.2022.9827231","DOIUrl":"https://doi.org/10.1109/iv51971.2022.9827231","url":null,"abstract":"For safe and comfortable navigation of autonomous vehicles, it is crucial to know the pedestrian’s intention of crossing the street. Generally, human drivers are aware of the traffic objects (e.g., crosswalks and traffic lights) in the environment while driving; likewise, these objects would play a crucial role for autonomous vehicles. In this research, we propose a novel pedestrian intention estimation method that not only takes into account the influence of traffic objects but also learns their contribution levels on the intention of the pedestrian. Our proposed method, referred to as Scene SpatioTemporal Graph Convolutional Network (Scene-STGCN), takes benefits from the strength of Graph Convolutional Networks and efficiently encodes the relationships between the pedestrian and the scene objects both spatially and temporally. We conduct several experiments on the Pedestrian Intention Estimation (PIE) dataset and illustrate the importance of scene objects and their contribution levels in the task of pedestrian intention estimation. Furthermore, we perform statistical analysis on the relevance of different traffic objects in the PIE dataset and carry out an ablation study on the effect of various information sources in the scene. Finally, we demonstrate the significance of the proposed Scene-STGCN through experimental comparisons with several baselines. The results indicate that our proposed Scene-STGCN outperforms the current state-of-the-art method by 0.03 in terms of ROC-AUC metric.","PeriodicalId":184622,"journal":{"name":"2022 IEEE Intelligent Vehicles Symposium (IV)","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114215565","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}
Pub Date : 2022-06-05DOI: 10.1109/iv51971.2022.9827233
Wenjun Liu, Yulin Zhai, Guang Chen, Alois Knoll
Model predictive control (MPC) is a commonly applied vehicle control technique, but its performance depends highly on how accurate the model captures the vehicle dynamics. It is disreputable hard to get a precise vehicle model in complex situations. The unmodeled dynamic will cause the uncertainty of the prediction which brings the risk while overtaking. To address this issue, Gaussian process (GP) regression is employed to acquire the unexplored discrepancy between the nominal vehicle model and the real vehicle dynamics which can result in a more accurate model. To achieve safe overtaking at highway curves, the constraint conditions are carefully designed. The implementation of GP-based MPC including approximate uncertainty propagation and safety constraints ensures that the ego vehicle overtakes the obstacles without collision. Simulation results show that GP-based MPC has a strong adaptability to different scenarios and outperforms MPC in overtaking control.
{"title":"Gaussian Process based Model Predictive Control for Overtaking Scenarios at Highway Curves","authors":"Wenjun Liu, Yulin Zhai, Guang Chen, Alois Knoll","doi":"10.1109/iv51971.2022.9827233","DOIUrl":"https://doi.org/10.1109/iv51971.2022.9827233","url":null,"abstract":"Model predictive control (MPC) is a commonly applied vehicle control technique, but its performance depends highly on how accurate the model captures the vehicle dynamics. It is disreputable hard to get a precise vehicle model in complex situations. The unmodeled dynamic will cause the uncertainty of the prediction which brings the risk while overtaking. To address this issue, Gaussian process (GP) regression is employed to acquire the unexplored discrepancy between the nominal vehicle model and the real vehicle dynamics which can result in a more accurate model. To achieve safe overtaking at highway curves, the constraint conditions are carefully designed. The implementation of GP-based MPC including approximate uncertainty propagation and safety constraints ensures that the ego vehicle overtakes the obstacles without collision. Simulation results show that GP-based MPC has a strong adaptability to different scenarios and outperforms MPC in overtaking control.","PeriodicalId":184622,"journal":{"name":"2022 IEEE Intelligent Vehicles Symposium (IV)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114985184","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}
Pub Date : 2022-06-05DOI: 10.1109/iv51971.2022.9827429
Alexandru Kampmann, Maximilian Lüer, S. Kowalewski, Bassam Alrifaee
This paper presents an approach for allocation of resources in an automotive service-oriented software architecture. Using mathematical optimization, we assign computational resources of an automotive compute cluster to a set of software services. Additionally, scheduling parameters of services are optimized under the consideration of dependencies between data flows and computations within services. The optimization minimizes power consumption and the maximum execution times of critical effect chains in a multi-objective optimization problem. The evaluation investigates the achievable reduction in power consumption using an exemplary system. Furthermore, we demonstrate a sharp reduction in maximum execution times of effect chains that span multiple services and ECUs.
{"title":"Optimization-based Resource Allocation for an Automotive Service-oriented Software Architecture","authors":"Alexandru Kampmann, Maximilian Lüer, S. Kowalewski, Bassam Alrifaee","doi":"10.1109/iv51971.2022.9827429","DOIUrl":"https://doi.org/10.1109/iv51971.2022.9827429","url":null,"abstract":"This paper presents an approach for allocation of resources in an automotive service-oriented software architecture. Using mathematical optimization, we assign computational resources of an automotive compute cluster to a set of software services. Additionally, scheduling parameters of services are optimized under the consideration of dependencies between data flows and computations within services. The optimization minimizes power consumption and the maximum execution times of critical effect chains in a multi-objective optimization problem. The evaluation investigates the achievable reduction in power consumption using an exemplary system. Furthermore, we demonstrate a sharp reduction in maximum execution times of effect chains that span multiple services and ECUs.","PeriodicalId":184622,"journal":{"name":"2022 IEEE Intelligent Vehicles Symposium (IV)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134478220","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}
Pub Date : 2022-06-05DOI: 10.1109/iv51971.2022.9827439
J. Lindner, G. Grigoropoulos, A. Keler, Patrick Malcolm, Florian Denk, Pascal Brunner, K. Bogenberger
In order to facilitate safe interactions between automated vehicles (AVs) and vulnerable road users (VRUs) such as bicyclists, we present a communication application for mobile devices that allows an AV or its passenger and a bicyclist to interact in certain traffic scenarios. At the intersection, the AV or its passenger can change the existing right-of-way rules to prioritise the ego-vehicle or the bicyclist. In a coupled driving simulator in which these two road users can interact, 16 proof-of-concept experiments are conducted. It is found that the perceived safety at conflict points can be increased through the use of the application. An investigation of the user data provides insights into the AV passengers’ decision types and duration in the scenarios studied. Moreover, the simulation results are used to revise and further develop the application concept.
{"title":"A mobile application for resolving bicyclist and automated vehicle interactions at intersections*","authors":"J. Lindner, G. Grigoropoulos, A. Keler, Patrick Malcolm, Florian Denk, Pascal Brunner, K. Bogenberger","doi":"10.1109/iv51971.2022.9827439","DOIUrl":"https://doi.org/10.1109/iv51971.2022.9827439","url":null,"abstract":"In order to facilitate safe interactions between automated vehicles (AVs) and vulnerable road users (VRUs) such as bicyclists, we present a communication application for mobile devices that allows an AV or its passenger and a bicyclist to interact in certain traffic scenarios. At the intersection, the AV or its passenger can change the existing right-of-way rules to prioritise the ego-vehicle or the bicyclist. In a coupled driving simulator in which these two road users can interact, 16 proof-of-concept experiments are conducted. It is found that the perceived safety at conflict points can be increased through the use of the application. An investigation of the user data provides insights into the AV passengers’ decision types and duration in the scenarios studied. Moreover, the simulation results are used to revise and further develop the application concept.","PeriodicalId":184622,"journal":{"name":"2022 IEEE Intelligent Vehicles Symposium (IV)","volume":"209 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133281234","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}
Pub Date : 2022-06-05DOI: 10.1109/iv51971.2022.9827236
Hao Long, Arash Khalatbarisoltani, Xiaosong Hu
Advances in connected automated technology allow for more efficient driving in heavy-duty transportation. By well coordinating the longitudinal movements of multiple vehicles driving in a string, eco-platooning control can significantly improve the driving comfort and fuel economy. Moreover, benefitting from the short following distances of the platoon members, the aerodynamic effects are believed to further reduce the overall energy consumption in heavy-duty applications. In this paper, we develop an aerodynamically aware cooperative adaptive cruise control (CACC) strategy based on nonlinear model predictive control (NMPC). The proposed strategy is implemented under different communication topologies: 1) predecessor following (PF), 2) leader following (LF), and 3) predecessor-leader following (PLF). The performance of three communication topologies is evaluated through several indexes, and the simulation results indicate that when the information of the platoon leader is broadcast to the other platoon members, resulting in a so-called LF or PLF topology, the string stability would be guaranteed, and the proposed strategy can improve the driving comfort of all three trucks by eliminating unnecessary accelerations. On the other hand, a remarkable decrement on demanded power can be derived due to the effect of air-drag reduction.
{"title":"MPC-based Eco-Platooning for Homogeneous Connected Trucks Under Different Communication Topologies","authors":"Hao Long, Arash Khalatbarisoltani, Xiaosong Hu","doi":"10.1109/iv51971.2022.9827236","DOIUrl":"https://doi.org/10.1109/iv51971.2022.9827236","url":null,"abstract":"Advances in connected automated technology allow for more efficient driving in heavy-duty transportation. By well coordinating the longitudinal movements of multiple vehicles driving in a string, eco-platooning control can significantly improve the driving comfort and fuel economy. Moreover, benefitting from the short following distances of the platoon members, the aerodynamic effects are believed to further reduce the overall energy consumption in heavy-duty applications. In this paper, we develop an aerodynamically aware cooperative adaptive cruise control (CACC) strategy based on nonlinear model predictive control (NMPC). The proposed strategy is implemented under different communication topologies: 1) predecessor following (PF), 2) leader following (LF), and 3) predecessor-leader following (PLF). The performance of three communication topologies is evaluated through several indexes, and the simulation results indicate that when the information of the platoon leader is broadcast to the other platoon members, resulting in a so-called LF or PLF topology, the string stability would be guaranteed, and the proposed strategy can improve the driving comfort of all three trucks by eliminating unnecessary accelerations. On the other hand, a remarkable decrement on demanded power can be derived due to the effect of air-drag reduction.","PeriodicalId":184622,"journal":{"name":"2022 IEEE Intelligent Vehicles Symposium (IV)","volume":"222 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133736431","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}
Pub Date : 2022-06-05DOI: 10.1109/iv51971.2022.9827456
Sadullah Goncu, Mehmet Ali Silgu, H. B. Çelikoglu
The term “Mobility” is gaining new perspectives. Due to the paradigm shift driven by information technologies and autonomous vehicles, on-demand mobility services have experienced significant growth. Operating such a service efficiently is a challenging task. Especially, assigning vehicles to customers plays a vital role in this regard. To meet a satisfactory level of service while keeping the operational costs to a minimum requires efficient assignment strategies. Work summarized in this paper utilizes several shared and non-shared assignment algorithms in order to propose a methodology to assess the effects on the overall system performance for an Autonomous Mobility on Demand system. Selected algorithms are tested in a theoretical network with real-world taxi data with the help of microscopic traffic simulation software Simulation of Urban Mobility. Simulation scenarios are generated for both varying demand levels and increasing fleet sizes. Results suggest that for high demand levels and small fleet sizes, shared algorithms outperform non-shared algorithms for every performance measure chosen: total vehicle kilometers traveled, the ratio of empty fleet kilometers, average passenger waiting time for pick up, and the number of customers served in a period.
“流动性”一词正在获得新的视角。由于信息技术和自动驾驶汽车驱动的模式转变，按需移动服务经历了显着增长。有效地运营这样的服务是一项具有挑战性的任务。特别是，为客户分配车辆在这方面起着至关重要的作用。为了达到令人满意的服务水平，同时将运营成本降至最低，需要有效的分配策略。本文总结的工作利用了几种共享和非共享分配算法，以提出一种方法来评估自动随需移动系统对整体系统性能的影响。在微观交通仿真软件simulation of Urban Mobility的帮助下，在一个具有真实出租车数据的理论网络中对所选算法进行了测试。为不同的需求水平和不断增加的车队规模生成模拟场景。结果表明，对于高需求水平和小车队规模，共享算法在选择的每个性能指标上都优于非共享算法:车辆行驶总公里数、空车队公里数比例、平均乘客等待接送时间和一段时间内服务的客户数量。
{"title":"Evaluation of Vehicle Assignment Algorithms for Autonomous Mobility on Demand","authors":"Sadullah Goncu, Mehmet Ali Silgu, H. B. Çelikoglu","doi":"10.1109/iv51971.2022.9827456","DOIUrl":"https://doi.org/10.1109/iv51971.2022.9827456","url":null,"abstract":"The term “Mobility” is gaining new perspectives. Due to the paradigm shift driven by information technologies and autonomous vehicles, on-demand mobility services have experienced significant growth. Operating such a service efficiently is a challenging task. Especially, assigning vehicles to customers plays a vital role in this regard. To meet a satisfactory level of service while keeping the operational costs to a minimum requires efficient assignment strategies. Work summarized in this paper utilizes several shared and non-shared assignment algorithms in order to propose a methodology to assess the effects on the overall system performance for an Autonomous Mobility on Demand system. Selected algorithms are tested in a theoretical network with real-world taxi data with the help of microscopic traffic simulation software Simulation of Urban Mobility. Simulation scenarios are generated for both varying demand levels and increasing fleet sizes. Results suggest that for high demand levels and small fleet sizes, shared algorithms outperform non-shared algorithms for every performance measure chosen: total vehicle kilometers traveled, the ratio of empty fleet kilometers, average passenger waiting time for pick up, and the number of customers served in a period.","PeriodicalId":184622,"journal":{"name":"2022 IEEE Intelligent Vehicles Symposium (IV)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133917185","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}
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