Pub Date : 2022-06-05DOI: 10.1109/iv51971.2022.9827213
Jamila Josip Borda, K. Matheus, F. Gerfers
This research work focuses on electrical investigations and characterization of the Automotive Ethernet channel for 25Gbps (25GBASE-T1). This characterization is performed with the aid of insertion loss ($mathrm{S}_{mathrm{DD}12}/mathrm{S}_{mathrm{DD}21}$) mixed-mode scattering parameters (S-parameters) which describe the transmitted signal electrical behavior within the Ethernet channel considering it’s coupled transmission line characteristics. This paper commences with an introductory background of this research topic. This is then followed with an overview of the Automotive Ethernet channel and components. A succeeding section addresses the various channel electrical characteristic parameters. With the aid of implemented multi-gigabit Ethernet test boards, to emulate an ECU-ECU communication system setup, the fourth section investigates and discusses insertion loss test bench measurements and simulations on channel segments (PCB, link segment) and complete single 25Gbps (25GBASE-T1) Ethernet channel. The investigations in this study deploy Shielded Twisted Pair (STP) cables of varying length and cable topologies as a physical transmission medium. Last section addresses the key takeaways of this paper and recommendations on subsequent analysis.
{"title":"Beyond 10Gbps Electrical Automotive Ethernet Channel Insertion Loss Characterization","authors":"Jamila Josip Borda, K. Matheus, F. Gerfers","doi":"10.1109/iv51971.2022.9827213","DOIUrl":"https://doi.org/10.1109/iv51971.2022.9827213","url":null,"abstract":"This research work focuses on electrical investigations and characterization of the Automotive Ethernet channel for 25Gbps (25GBASE-T1). This characterization is performed with the aid of insertion loss ($mathrm{S}_{mathrm{DD}12}/mathrm{S}_{mathrm{DD}21}$) mixed-mode scattering parameters (S-parameters) which describe the transmitted signal electrical behavior within the Ethernet channel considering it’s coupled transmission line characteristics. This paper commences with an introductory background of this research topic. This is then followed with an overview of the Automotive Ethernet channel and components. A succeeding section addresses the various channel electrical characteristic parameters. With the aid of implemented multi-gigabit Ethernet test boards, to emulate an ECU-ECU communication system setup, the fourth section investigates and discusses insertion loss test bench measurements and simulations on channel segments (PCB, link segment) and complete single 25Gbps (25GBASE-T1) Ethernet channel. The investigations in this study deploy Shielded Twisted Pair (STP) cables of varying length and cable topologies as a physical transmission medium. Last section addresses the key takeaways of this paper and recommendations on subsequent analysis.","PeriodicalId":184622,"journal":{"name":"2022 IEEE Intelligent Vehicles Symposium (IV)","volume":"53 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":"122193650","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/iv51971.2022.9827445
M. Joerger, Julian Wang, A. Hassani
In this paper, we develop and evaluate a Convolutional Neural Network (CNN)-based Light Detection and Ranging (LiDAR) localization algorithm that includes uncertainty quantification for ground vehicle navigation. This paper builds upon prior research where we used a CNN to estimate a rover’s position and orientation (pose) using LiDAR point clouds (PCs). This paper presents a simplification of the LiDAR PC processing and describes a new approach for outputting a covariance matrix in addition to the rover pose estimates. Performance assessment is carried out in a structured, static lab environment using a LiDAR-equipped rover moving along a fixed, repeated trajectory.
{"title":"On Uncertainty Quantification for Convolutional Neural Network LiDAR Localization","authors":"M. Joerger, Julian Wang, A. Hassani","doi":"10.1109/iv51971.2022.9827445","DOIUrl":"https://doi.org/10.1109/iv51971.2022.9827445","url":null,"abstract":"In this paper, we develop and evaluate a Convolutional Neural Network (CNN)-based Light Detection and Ranging (LiDAR) localization algorithm that includes uncertainty quantification for ground vehicle navigation. This paper builds upon prior research where we used a CNN to estimate a rover’s position and orientation (pose) using LiDAR point clouds (PCs). This paper presents a simplification of the LiDAR PC processing and describes a new approach for outputting a covariance matrix in addition to the rover pose estimates. Performance assessment is carried out in a structured, static lab environment using a LiDAR-equipped rover moving along a fixed, repeated trajectory.","PeriodicalId":184622,"journal":{"name":"2022 IEEE Intelligent Vehicles Symposium (IV)","volume":"14 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":"129866045","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/iv51971.2022.9827200
Geesung Oh, Joon-Sang Park, Kyunghun Hwang, Sejoon Lim
It is necessary to calibrate the hydraulic pressure of the shift control to develop an automatic transmission (AT), and this calibration process entails a subjective shift quality assessment by experienced engineers. An objective shift quality assessment methodology has been explored for a long time to replace the engineer. The most recent data-based assessment model has attained a nearly human-like performance. However, preparing the large number of data labels required for supervised learning of the model has limitations. This study proposes an unsupervised anomaly detection model for objective shift quality assessment to address data label shortages and high data labeling costs. The proposed anomaly detection model is trained to classify a normal shift and an abnormal shift using just normal shift data. It is possible to easily obtain many train datasets from ordinary vehicles, and data labeling is not required. On the basis of real vehicle shift data, multiple anomaly detection models composed of various deep neural networks are developed and assessed. The evaluation results show that training exclusively on normal shift data can detect abnormal shifts; the best area under receiver operating characteristic curve is 0.902.
{"title":"Unsupervised Anomaly Detection Approach for Shift Quality Assessment Using Deep Neural Networks","authors":"Geesung Oh, Joon-Sang Park, Kyunghun Hwang, Sejoon Lim","doi":"10.1109/iv51971.2022.9827200","DOIUrl":"https://doi.org/10.1109/iv51971.2022.9827200","url":null,"abstract":"It is necessary to calibrate the hydraulic pressure of the shift control to develop an automatic transmission (AT), and this calibration process entails a subjective shift quality assessment by experienced engineers. An objective shift quality assessment methodology has been explored for a long time to replace the engineer. The most recent data-based assessment model has attained a nearly human-like performance. However, preparing the large number of data labels required for supervised learning of the model has limitations. This study proposes an unsupervised anomaly detection model for objective shift quality assessment to address data label shortages and high data labeling costs. The proposed anomaly detection model is trained to classify a normal shift and an abnormal shift using just normal shift data. It is possible to easily obtain many train datasets from ordinary vehicles, and data labeling is not required. On the basis of real vehicle shift data, multiple anomaly detection models composed of various deep neural networks are developed and assessed. The evaluation results show that training exclusively on normal shift data can detect abnormal shifts; the best area under receiver operating characteristic curve is 0.902.","PeriodicalId":184622,"journal":{"name":"2022 IEEE Intelligent Vehicles Symposium (IV)","volume":"19 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":"129879679","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: 10.48550/arXiv.2206.05158
Julian Schmidt, Julian Jordan, D. Raba, Tobias Welz, K. Dietmayer
Advances in learning-based trajectory prediction are enabled by large-scale datasets. However, in-depth analysis of such datasets is limited. Moreover, the evaluation of prediction models is limited to metrics averaged over all samples in the dataset. We propose an automated methodology that allows to extract maneuvers (e.g., left turn, lane change) from agent trajectories in such datasets. The methodology considers information about the agent dynamics and information about the lane segments the agent traveled along. Although it is possible to use the resulting maneuvers for training classification networks, we exemplary use them for extensive trajectory dataset analysis and maneuver-specific evaluation of multiple state-of-the-art trajectory prediction models. Additionally, an analysis of the datasets and an evaluation of the prediction models based on the agent dynamics is provided.
{"title":"MEAT: Maneuver Extraction from Agent Trajectories","authors":"Julian Schmidt, Julian Jordan, D. Raba, Tobias Welz, K. Dietmayer","doi":"10.48550/arXiv.2206.05158","DOIUrl":"https://doi.org/10.48550/arXiv.2206.05158","url":null,"abstract":"Advances in learning-based trajectory prediction are enabled by large-scale datasets. However, in-depth analysis of such datasets is limited. Moreover, the evaluation of prediction models is limited to metrics averaged over all samples in the dataset. We propose an automated methodology that allows to extract maneuvers (e.g., left turn, lane change) from agent trajectories in such datasets. The methodology considers information about the agent dynamics and information about the lane segments the agent traveled along. Although it is possible to use the resulting maneuvers for training classification networks, we exemplary use them for extensive trajectory dataset analysis and maneuver-specific evaluation of multiple state-of-the-art trajectory prediction models. Additionally, an analysis of the datasets and an evaluation of the prediction models based on the agent dynamics is provided.","PeriodicalId":184622,"journal":{"name":"2022 IEEE Intelligent Vehicles Symposium (IV)","volume":"1 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":"128291616","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/iv51971.2022.9827431
M. Kloock, Matthis Dirksen, S. Kowalewski, Bassam Alrifaee
This paper presents a method for generating coupling topologies for multi-agent systems. Our method is based on a non-cooperative game in which each agent chooses couplings to activate or deactivate using a utility function. The utility function measures the importance of agents to one another and enables conflict avoidance in distributed decision-making. Depending on the application’s needs, our method is able to generate unidirectional or bidirectional couplings. In our evaluation, we used car-like robots in a simulation environment. It shows that the generated coupling topologies are applicable to the domain of networked and autonomous vehicles.
{"title":"Generation of Coupling Topologies for Multi-Agent Systems using Non-Cooperative Games","authors":"M. Kloock, Matthis Dirksen, S. Kowalewski, Bassam Alrifaee","doi":"10.1109/iv51971.2022.9827431","DOIUrl":"https://doi.org/10.1109/iv51971.2022.9827431","url":null,"abstract":"This paper presents a method for generating coupling topologies for multi-agent systems. Our method is based on a non-cooperative game in which each agent chooses couplings to activate or deactivate using a utility function. The utility function measures the importance of agents to one another and enables conflict avoidance in distributed decision-making. Depending on the application’s needs, our method is able to generate unidirectional or bidirectional couplings. In our evaluation, we used car-like robots in a simulation environment. It shows that the generated coupling topologies are applicable to the domain of networked and autonomous vehicles.","PeriodicalId":184622,"journal":{"name":"2022 IEEE Intelligent Vehicles Symposium (IV)","volume":"4 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":"128437095","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/iv51971.2022.9827357
Yuan-Chuen Lin, M. Althoff
Autonomous vehicles must comply with traffic rules. However, most motion planners do not explicitly consider all relevant traffic rules. Once traffic rule violations of an initially-planned trajectory are detected, there is often not enough time to replan the entire trajectory. To solve this problem, we propose to repair the initial trajectory by investigating the satisfiability modulo theories paradigm. This framework makes it efficient to reason whether and how the trajectory can be repaired and, at the same time, determine the part along the trajectory that can remain unchanged. Moreover, the robustness of traffic rule satisfaction is used to formulate a convex optimization problem for generating rule-compliant trajectories. We compare our approach with trajectory replanning and demonstrate its usefulness with traffic scenarios from the CommonRoad benchmark suite and recorded data. The evaluation result shows that rule-compliant trajectory repairing is computationally efficient and widely applicable.
{"title":"Rule-Compliant Trajectory Repairing using Satisfiability Modulo Theories","authors":"Yuan-Chuen Lin, M. Althoff","doi":"10.1109/iv51971.2022.9827357","DOIUrl":"https://doi.org/10.1109/iv51971.2022.9827357","url":null,"abstract":"Autonomous vehicles must comply with traffic rules. However, most motion planners do not explicitly consider all relevant traffic rules. Once traffic rule violations of an initially-planned trajectory are detected, there is often not enough time to replan the entire trajectory. To solve this problem, we propose to repair the initial trajectory by investigating the satisfiability modulo theories paradigm. This framework makes it efficient to reason whether and how the trajectory can be repaired and, at the same time, determine the part along the trajectory that can remain unchanged. Moreover, the robustness of traffic rule satisfaction is used to formulate a convex optimization problem for generating rule-compliant trajectories. We compare our approach with trajectory replanning and demonstrate its usefulness with traffic scenarios from the CommonRoad benchmark suite and recorded data. The evaluation result shows that rule-compliant trajectory repairing is computationally efficient and widely applicable.","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":"128571793","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 eco-driving strategy that targets driving speed optimization is recognized as a promising technique to improve vehicle energy efficiency. However, it is difficult to achieve real-time eco-driving control of hybrid electric vehicle (HEV) since the speed optimization and powertrain energy management should be resolved simultaneously. This paper proposes a hierarchical control architecture consisting of learning-based velocity planner and real-time energy management system. In the upper stage, Proximal Policy optimization (PPO) agent is trained to generate acceleration which meets multiple control objectives. The lower stage adopts Equivalent Consumption Minimization Strategy (ECMS) for real-time power split control considering powertrain dynamics. Finally, the eco-driving simulations of six signalized intersections in Nanjing are conducted. Compared with two different rule-based strategies, the proposed control architecture can achieve at least 7.39% of fuel economy saving and avoid a significant drop in the battery state of charge at the expense of higher than 5% of travel time. Simulation results also prove that the proposed strategy has an energy-saving potential in unseen scenarios.
{"title":"Learning-based Eco-driving Strategy Design for Connected Power-split Hybrid Electric Vehicles at signalized corridors","authors":"Zhihan Li, Weichao Zhuang, Guo-dong Yin, Fei Ju, Qun Wang, Haonan Ding","doi":"10.1109/iv51971.2022.9827278","DOIUrl":"https://doi.org/10.1109/iv51971.2022.9827278","url":null,"abstract":"The eco-driving strategy that targets driving speed optimization is recognized as a promising technique to improve vehicle energy efficiency. However, it is difficult to achieve real-time eco-driving control of hybrid electric vehicle (HEV) since the speed optimization and powertrain energy management should be resolved simultaneously. This paper proposes a hierarchical control architecture consisting of learning-based velocity planner and real-time energy management system. In the upper stage, Proximal Policy optimization (PPO) agent is trained to generate acceleration which meets multiple control objectives. The lower stage adopts Equivalent Consumption Minimization Strategy (ECMS) for real-time power split control considering powertrain dynamics. Finally, the eco-driving simulations of six signalized intersections in Nanjing are conducted. Compared with two different rule-based strategies, the proposed control architecture can achieve at least 7.39% of fuel economy saving and avoid a significant drop in the battery state of charge at the expense of higher than 5% of travel time. Simulation results also prove that the proposed strategy has an energy-saving potential in unseen scenarios.","PeriodicalId":184622,"journal":{"name":"2022 IEEE Intelligent Vehicles Symposium (IV)","volume":"13 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":"129155384","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/iv51971.2022.9827165
Teresa Rock, M. Bahram, Chantal Himmels, S. Marker
Driving simulation is becoming an increasingly important component of research and development in the automotive industry. When performing simulator studies in urban scenarios, the challenge is to create a realistic driving context including natural interactions between the subject and artificial traffic participants, which are simulated by agent models. These traffic agents should behave as similar as possible to real humans. This raises the question of how to define realistic or human-like behaviour of traffic agents and how to measure this. Furthermore, it is necessary to investigate the influence of the surrounding traffic on the driver’s behaviour and perception of reality in the simulator. Accordingly, we present a method for quantifying the degree of realism of virtual traffic agents’ behaviour and their impact on subjects’ experience in a simulator experiment. By means of questionnaires, participants rated their perception of reality and the behaviour of present agent models. The experiment shows that surrounding traffic has a positive effect on subjects’ perception and behaviour, indicating that more realistic traffic agents have the potential to improve the validity of simulator studies. Moreover, our results provide new insights regarding required characteristics for the development of human-like traffic agents and give an overview of current strengths and weaknesses.
{"title":"Quantifying Realistic Behaviour of Traffic Agents in Urban Driving Simulation Based on Questionnaires","authors":"Teresa Rock, M. Bahram, Chantal Himmels, S. Marker","doi":"10.1109/iv51971.2022.9827165","DOIUrl":"https://doi.org/10.1109/iv51971.2022.9827165","url":null,"abstract":"Driving simulation is becoming an increasingly important component of research and development in the automotive industry. When performing simulator studies in urban scenarios, the challenge is to create a realistic driving context including natural interactions between the subject and artificial traffic participants, which are simulated by agent models. These traffic agents should behave as similar as possible to real humans. This raises the question of how to define realistic or human-like behaviour of traffic agents and how to measure this. Furthermore, it is necessary to investigate the influence of the surrounding traffic on the driver’s behaviour and perception of reality in the simulator. Accordingly, we present a method for quantifying the degree of realism of virtual traffic agents’ behaviour and their impact on subjects’ experience in a simulator experiment. By means of questionnaires, participants rated their perception of reality and the behaviour of present agent models. The experiment shows that surrounding traffic has a positive effect on subjects’ perception and behaviour, indicating that more realistic traffic agents have the potential to improve the validity of simulator studies. Moreover, our results provide new insights regarding required characteristics for the development of human-like traffic agents and give an overview of current strengths and weaknesses.","PeriodicalId":184622,"journal":{"name":"2022 IEEE Intelligent Vehicles Symposium (IV)","volume":"84 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":"124631181","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/iv51971.2022.9827152
Yanggu Zheng, Barys Shyrokau, T. Keviczky
Motion comfort is the basis of many societal benefits promised by automated driving and motion planning is primarily responsible for this. By planning the spatial trajectory and the velocity profile, motion planners can significantly enhance motion comfort, ideally without sacrificing time efficiency. Active suspensions can push the boundary further by enabling additional degrees of freedom in the controllable vehicle motions. In this paper, we propose to integrate the planning of roll motion into an optimization-based motion planning algorithm called 3DOP(3 Degrees-of-Freedom Optimal Planning), where the conflicting objectives of comfort and time efficiency are optimized. The feasibility of the planned motion is verified in a realistic simulation environment, where feedforward-proportional control suffices to track the speed, path, and roll references. The proposed scheme achieves a significant reduction of motion discomfort, namely by up to 28.1% over the variant without controllable roll motion, or up to 34.2% over an acceleration-bounded driver model. The results suggest considerable potential for improving motion comfort by equipping automated vehicles with active suspensions.
{"title":"3DOP: Comfort-oriented Motion Planning for Automated Vehicles with Active Suspensions","authors":"Yanggu Zheng, Barys Shyrokau, T. Keviczky","doi":"10.1109/iv51971.2022.9827152","DOIUrl":"https://doi.org/10.1109/iv51971.2022.9827152","url":null,"abstract":"Motion comfort is the basis of many societal benefits promised by automated driving and motion planning is primarily responsible for this. By planning the spatial trajectory and the velocity profile, motion planners can significantly enhance motion comfort, ideally without sacrificing time efficiency. Active suspensions can push the boundary further by enabling additional degrees of freedom in the controllable vehicle motions. In this paper, we propose to integrate the planning of roll motion into an optimization-based motion planning algorithm called 3DOP(3 Degrees-of-Freedom Optimal Planning), where the conflicting objectives of comfort and time efficiency are optimized. The feasibility of the planned motion is verified in a realistic simulation environment, where feedforward-proportional control suffices to track the speed, path, and roll references. The proposed scheme achieves a significant reduction of motion discomfort, namely by up to 28.1% over the variant without controllable roll motion, or up to 34.2% over an acceleration-bounded driver model. The results suggest considerable potential for improving motion comfort by equipping automated vehicles with active suspensions.","PeriodicalId":184622,"journal":{"name":"2022 IEEE Intelligent Vehicles Symposium (IV)","volume":"27 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":"127031685","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/iv51971.2022.9827017
Yuncheng Jiang, Zenghui Liu, Danjian Qian, Hao Zuo, Weiliang He, Jun Wang
In urban driving scenarios, it is a key component for autonomous vehicles to generate a smooth, kinodynamically feasible, and collision-free path. We present an optimization-based path planning method for autonomous vehicles navigating in cluttered environment, e.g., roads partially blocked by static or moving obstacles. Our method first computes a collision-free reference line using quadratic programming(QP), and then using the reference line as initial guess to generate a smooth and feasible path by iterative optimization using sequential quadratic programming(SQP). It works within a fractions of a second, thus permitting efficient regeneration.
{"title":"Robust Online Path Planning for Autonomous Vehicle Using Sequential Quadratic Programming","authors":"Yuncheng Jiang, Zenghui Liu, Danjian Qian, Hao Zuo, Weiliang He, Jun Wang","doi":"10.1109/iv51971.2022.9827017","DOIUrl":"https://doi.org/10.1109/iv51971.2022.9827017","url":null,"abstract":"In urban driving scenarios, it is a key component for autonomous vehicles to generate a smooth, kinodynamically feasible, and collision-free path. We present an optimization-based path planning method for autonomous vehicles navigating in cluttered environment, e.g., roads partially blocked by static or moving obstacles. Our method first computes a collision-free reference line using quadratic programming(QP), and then using the reference line as initial guess to generate a smooth and feasible path by iterative optimization using sequential quadratic programming(SQP). It works within a fractions of a second, thus permitting efficient regeneration.","PeriodicalId":184622,"journal":{"name":"2022 IEEE Intelligent Vehicles Symposium (IV)","volume":"5 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":"116345981","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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