Pub Date : 2022-06-05DOI: 10.1109/iv51971.2022.9827153
Sebastian Maierhofer, Paul Moosbrugger, M. Althoff
Intersections are difficult to navigate for both human drivers and autonomous vehicles because several diverse traffic rules must be considered. In addition, current traffic rules are ambiguous and cannot be applied directly by autonomous vehicles. Therefore, national traffic rules must be concretized and formalized so that they are machine-interpretable. We present formalized intersection traffic rules in temporal logic and use the German traffic regulations as a concrete example. Our formalization considers different types of intersections, i.e., signalized, traffic-sign-regulated, and unregulated intersections. We also define predicates and functions that can be easily reused for other national traffic laws. We evaluate our formalized traffic rules on recorded real-world scenarios and manually-created test scenarios. Our evaluation validates the formalization from different legal sources.
{"title":"Formalization of Intersection Traffic Rules in Temporal Logic","authors":"Sebastian Maierhofer, Paul Moosbrugger, M. Althoff","doi":"10.1109/iv51971.2022.9827153","DOIUrl":"https://doi.org/10.1109/iv51971.2022.9827153","url":null,"abstract":"Intersections are difficult to navigate for both human drivers and autonomous vehicles because several diverse traffic rules must be considered. In addition, current traffic rules are ambiguous and cannot be applied directly by autonomous vehicles. Therefore, national traffic rules must be concretized and formalized so that they are machine-interpretable. We present formalized intersection traffic rules in temporal logic and use the German traffic regulations as a concrete example. Our formalization considers different types of intersections, i.e., signalized, traffic-sign-regulated, and unregulated intersections. We also define predicates and functions that can be easily reused for other national traffic laws. We evaluate our formalized traffic rules on recorded real-world scenarios and manually-created test scenarios. Our evaluation validates the formalization from different legal sources.","PeriodicalId":184622,"journal":{"name":"2022 IEEE Intelligent Vehicles Symposium (IV)","volume":"22 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":"124844130","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/iv51971.2022.9827284
T. Wengerter, Rodrigo Pérez, Erwin M. Biebl, J. Worms, D. O’Hagan
Frequency modulated continuous wave radars are an important component of modern driver assistance systems and enable safer automated driving. To achieve real time detection and classification of multiple road users in the range-Doppler map, the usage of neural target detection networks is proposed. Since the amount of labelled radar measurements available limits the training process, a new radar simulation framework is presented which generates arbitrary traffic scenarios with reflection models for pedestrians, bicyclists and vehicles. With an adaptive FMCW setup, sequences of dynamic urban multi-target radar measurements are simulated, maintaining minimum computational complexity. Solely trained on simulated measurement data, the neural network achieves an average precision above 87% on bicyclists and vehicles in real measurement data which is comparable to the performance of neural networks trained on real measurement datasets.
{"title":"Simulation of Urban Automotive Radar Measurements for Deep Learning Target Detection","authors":"T. Wengerter, Rodrigo Pérez, Erwin M. Biebl, J. Worms, D. O’Hagan","doi":"10.1109/iv51971.2022.9827284","DOIUrl":"https://doi.org/10.1109/iv51971.2022.9827284","url":null,"abstract":"Frequency modulated continuous wave radars are an important component of modern driver assistance systems and enable safer automated driving. To achieve real time detection and classification of multiple road users in the range-Doppler map, the usage of neural target detection networks is proposed. Since the amount of labelled radar measurements available limits the training process, a new radar simulation framework is presented which generates arbitrary traffic scenarios with reflection models for pedestrians, bicyclists and vehicles. With an adaptive FMCW setup, sequences of dynamic urban multi-target radar measurements are simulated, maintaining minimum computational complexity. Solely trained on simulated measurement data, the neural network achieves an average precision above 87% on bicyclists and vehicles in real measurement data which is comparable to the performance of neural networks trained on real measurement datasets.","PeriodicalId":184622,"journal":{"name":"2022 IEEE Intelligent Vehicles Symposium (IV)","volume":"113 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":"122671080","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/iv51971.2022.9827179
Anthony Deschenes, Jonathan Gaudreault, Claude-Guy Quimper
Predicting the time needed to charge an electric vehicle from X% to Y% is a difficult task due to the nonlinearity of the charging process and other external factors such as temperature and battery degradation. Using 28,000 real-life level 3 fast charging sessions from 15 different types of electric vehicles, we train models for this task. We compare learning models such as random forest, linear and seconddegree regressions, support vector regressions, and neural networks. The models take into consideration the external temperature, battery capacity, nominal capacity of the electric vehicle, number of charges made during the same day, maximum charging time allowed by the electric vehicle, target voltage, maximum voltage and maximum current asked by the electric vehicle. The models also take into consideration the vehicle type and the charging station type. We use a data augmentation technique (SMOTE) and hyperparameters optimization to enhance our model performances. The structure of the neural networks is optimized using Bayesian optimization. All models are trained and statistically compared in order to find the overall best model for all vehicle types. The overall best model is a neural network with a sub neural network pre-trained to predict the electric vehicle type.
{"title":"Predicting real life electric vehicle fast charging session duration using neural networks","authors":"Anthony Deschenes, Jonathan Gaudreault, Claude-Guy Quimper","doi":"10.1109/iv51971.2022.9827179","DOIUrl":"https://doi.org/10.1109/iv51971.2022.9827179","url":null,"abstract":"Predicting the time needed to charge an electric vehicle from X% to Y% is a difficult task due to the nonlinearity of the charging process and other external factors such as temperature and battery degradation. Using 28,000 real-life level 3 fast charging sessions from 15 different types of electric vehicles, we train models for this task. We compare learning models such as random forest, linear and seconddegree regressions, support vector regressions, and neural networks. The models take into consideration the external temperature, battery capacity, nominal capacity of the electric vehicle, number of charges made during the same day, maximum charging time allowed by the electric vehicle, target voltage, maximum voltage and maximum current asked by the electric vehicle. The models also take into consideration the vehicle type and the charging station type. We use a data augmentation technique (SMOTE) and hyperparameters optimization to enhance our model performances. The structure of the neural networks is optimized using Bayesian optimization. All models are trained and statistically compared in order to find the overall best model for all vehicle types. The overall best model is a neural network with a sub neural network pre-trained to predict the electric vehicle type.","PeriodicalId":184622,"journal":{"name":"2022 IEEE Intelligent Vehicles Symposium (IV)","volume":"105 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":"124774786","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/iv51971.2022.9827097
Felix Beringhoff, Joel Greenyer, Christian Roesener, Matthias Tichy
There is consensus across the automotive industry that Automated Driving Systems and automated vehicles challenge the way how quality assurance and, particularly, testing must be performed. However, there is a lack of up-to-date empirical studies that substantiate this concern. We conducted interviews with several experts from industry and research to systematically identify challenges as well as improvement opportunities in methods and tools. We report in this paper on 31 challenges that we identified in the areas of scenario- and simulation-based testing, test automation, and test execution. One recurrent challenge expressed by many experts is the problem how to translate a desired condition to be tested into an executable scenario model. This is not alone a question of scripting the scenario, but also of considering a vehicle under test that might try to evade the desired test condition.
{"title":"Thirty-One Challenges in Testing Automated Vehicles: Interviews with Experts from Industry and Research","authors":"Felix Beringhoff, Joel Greenyer, Christian Roesener, Matthias Tichy","doi":"10.1109/iv51971.2022.9827097","DOIUrl":"https://doi.org/10.1109/iv51971.2022.9827097","url":null,"abstract":"There is consensus across the automotive industry that Automated Driving Systems and automated vehicles challenge the way how quality assurance and, particularly, testing must be performed. However, there is a lack of up-to-date empirical studies that substantiate this concern. We conducted interviews with several experts from industry and research to systematically identify challenges as well as improvement opportunities in methods and tools. We report in this paper on 31 challenges that we identified in the areas of scenario- and simulation-based testing, test automation, and test execution. One recurrent challenge expressed by many experts is the problem how to translate a desired condition to be tested into an executable scenario model. This is not alone a question of scripting the scenario, but also of considering a vehicle under test that might try to evade the desired test condition.","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":"124780330","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/iv51971.2022.9827442
Matthew J. Eagon, Setayesh Fakhimi, George Lyu, Audrey Yang, B. Lin, W. Northrop
The development of battery electric vehicles (BEVs) is accelerating due to their environmental advantages over gasoline and diesel-powered vehicles, including a decrease in air pollution and an increase in energy efficiency. The deployment of charging infrastructure will need to increase to keep pace with demand, especially for large commercial vehicles for which few public chargers currently exist. In this paper, a new flexible framework is proposed for optimizing the placement of charging stations for BEVs, within which different physical models and optimization techniques may be used. Furthermore, a set of metrics is suggested to help enforce complex constraints and facilitate direct comparison between different optimization techniques. Unlike many existing charger placement techniques, the proposed method directly considers the historical driving patterns on a vehicle-by-vehicle basis, using transparent models to assess impacts of candidate charger placements, thus improving the explainability of the results. In the developed framework, modeled BEVs are first generated along the road network to mimic historical traffic data and are simulated traveling along a given route according to a simplified vehicle model. During the simulation, the charger placement problem is initially relaxed to allow vehicles to charge at any node along the road network, and vehicle states are tracked to assess areas of high charging demand. Charging stations are then placed based on the results of the relaxed simulation, and suggested placements are evaluated via road network simulation with fixed charger locations. This proposed framework is applied to a sample problem of placing charging stations along five major highway corridors for Class 8 over-the-road electric trucks. A novel mixed integer programming (MIP) formulation is proposed to optimize charger placements based upon the expected charging demand. Constraints were imposed on the final placement results to limit expected wait times at each station and ensure a minimum threshold of trucking routes are viable for BEVs. The results demonstrate the flexibility and potential effectiveness of the developed model-based framework for scalable charger station deployment.
{"title":"Model-Based Framework to Optimize Charger Station Deployment for Battery Electric Vehicles","authors":"Matthew J. Eagon, Setayesh Fakhimi, George Lyu, Audrey Yang, B. Lin, W. Northrop","doi":"10.1109/iv51971.2022.9827442","DOIUrl":"https://doi.org/10.1109/iv51971.2022.9827442","url":null,"abstract":"The development of battery electric vehicles (BEVs) is accelerating due to their environmental advantages over gasoline and diesel-powered vehicles, including a decrease in air pollution and an increase in energy efficiency. The deployment of charging infrastructure will need to increase to keep pace with demand, especially for large commercial vehicles for which few public chargers currently exist. In this paper, a new flexible framework is proposed for optimizing the placement of charging stations for BEVs, within which different physical models and optimization techniques may be used. Furthermore, a set of metrics is suggested to help enforce complex constraints and facilitate direct comparison between different optimization techniques. Unlike many existing charger placement techniques, the proposed method directly considers the historical driving patterns on a vehicle-by-vehicle basis, using transparent models to assess impacts of candidate charger placements, thus improving the explainability of the results. In the developed framework, modeled BEVs are first generated along the road network to mimic historical traffic data and are simulated traveling along a given route according to a simplified vehicle model. During the simulation, the charger placement problem is initially relaxed to allow vehicles to charge at any node along the road network, and vehicle states are tracked to assess areas of high charging demand. Charging stations are then placed based on the results of the relaxed simulation, and suggested placements are evaluated via road network simulation with fixed charger locations. This proposed framework is applied to a sample problem of placing charging stations along five major highway corridors for Class 8 over-the-road electric trucks. A novel mixed integer programming (MIP) formulation is proposed to optimize charger placements based upon the expected charging demand. Constraints were imposed on the final placement results to limit expected wait times at each station and ensure a minimum threshold of trucking routes are viable for BEVs. The results demonstrate the flexibility and potential effectiveness of the developed model-based framework for scalable charger station deployment.","PeriodicalId":184622,"journal":{"name":"2022 IEEE Intelligent Vehicles Symposium (IV)","volume":"44 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":"125515314","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/iv51971.2022.9827028
Barbara Gallazzi, Paolo Cudrano, Matteo Frosi, S. Mentasti, Matteo Matteucci
Lane-level HD maps are crucial for trajectory planning and control in current autonomous vehicles. For this reason, appropriate line models should be adopted to define them. Whereas mapping algorithms often rely on inaccurate representations, clothoid curves possess peculiar smoothness properties that make them desirable representations of road lines in control algorithms. We propose a multi-stage pipeline for the generation of lane-level HD maps from monocular vision relying on clothoidal spline models. We obtain measurements of the line positions using a line detection algorithm, and we exploit a graph-based optimization framework to reach an optimal fitting. An iterative greedy procedure reduces the model complexity removing unnecessary clothoids. We validate our system on a real-world dataset, which we make publicly available for further research at https://airlab.deib.polimi.it/datasets-and-tools/.
{"title":"Clothoidal Mapping of Road Line Markings for Autonomous Driving High-Definition Maps","authors":"Barbara Gallazzi, Paolo Cudrano, Matteo Frosi, S. Mentasti, Matteo Matteucci","doi":"10.1109/iv51971.2022.9827028","DOIUrl":"https://doi.org/10.1109/iv51971.2022.9827028","url":null,"abstract":"Lane-level HD maps are crucial for trajectory planning and control in current autonomous vehicles. For this reason, appropriate line models should be adopted to define them. Whereas mapping algorithms often rely on inaccurate representations, clothoid curves possess peculiar smoothness properties that make them desirable representations of road lines in control algorithms. We propose a multi-stage pipeline for the generation of lane-level HD maps from monocular vision relying on clothoidal spline models. We obtain measurements of the line positions using a line detection algorithm, and we exploit a graph-based optimization framework to reach an optimal fitting. An iterative greedy procedure reduces the model complexity removing unnecessary clothoids. We validate our system on a real-world dataset, which we make publicly available for further research at https://airlab.deib.polimi.it/datasets-and-tools/.","PeriodicalId":184622,"journal":{"name":"2022 IEEE Intelligent Vehicles Symposium (IV)","volume":"11257 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":"123347393","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/iv51971.2022.9827245
T. Geissler, Elisabeth Shi
Support from the physical and digital road infrastructure can extend the conditions under which connected and automated vehicles can operate safely. While there are separate concepts for the Operational Design Domain (ODD) and Infrastructure Support for Automated Driving (ISAD), there is no clear picture of their interplay yet. This paper suggests an integrated perspective on the challenge of cross-sector collaboration for the benefit of Connected, Cooperative and Automated Mobility (CCAM). Taxonomies are analyzed from three perspectives: the user, the vehicles and the road infrastructure. It is found that besides well-established concepts (SAE J 3016, Principles of Operations Framework) there is a number of emerging taxonomies which consistently fit into the overall collaboration landscape. These taxonomies include the user communication of automated driving, the cooperation classes (SAE J 3216), Infrastructure Support for Automated Driving (ISAD) and Levels of Service for Automated Driving (LOSAD), the latter two being recently proposed as elements of a Smart Roads Classification. It is concluded that the taxonomies should be used and applied as a shared understanding which calls for close collaboration between the actors in order to prepare, pilot, test and deploy Connected Cooperative and Automated Mobility (CCAM) services in the coming decade(s).
{"title":"Taxonomies of Connected, Cooperative and Automated Mobility","authors":"T. Geissler, Elisabeth Shi","doi":"10.1109/iv51971.2022.9827245","DOIUrl":"https://doi.org/10.1109/iv51971.2022.9827245","url":null,"abstract":"Support from the physical and digital road infrastructure can extend the conditions under which connected and automated vehicles can operate safely. While there are separate concepts for the Operational Design Domain (ODD) and Infrastructure Support for Automated Driving (ISAD), there is no clear picture of their interplay yet. This paper suggests an integrated perspective on the challenge of cross-sector collaboration for the benefit of Connected, Cooperative and Automated Mobility (CCAM). Taxonomies are analyzed from three perspectives: the user, the vehicles and the road infrastructure. It is found that besides well-established concepts (SAE J 3016, Principles of Operations Framework) there is a number of emerging taxonomies which consistently fit into the overall collaboration landscape. These taxonomies include the user communication of automated driving, the cooperation classes (SAE J 3216), Infrastructure Support for Automated Driving (ISAD) and Levels of Service for Automated Driving (LOSAD), the latter two being recently proposed as elements of a Smart Roads Classification. It is concluded that the taxonomies should be used and applied as a shared understanding which calls for close collaboration between the actors in order to prepare, pilot, test and deploy Connected Cooperative and Automated Mobility (CCAM) services in the coming decade(s).","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":"114970297","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/iv51971.2022.9827194
Mohamad Albilani, A. Bouzeghoub
Automated Parking Systems (APS) are responsible for performing a parking maneuver in a secure and time-efficient full autonomy.These systems include mainly three methods; parking spot exploration, path planning, and path tracking. In the literature, there are several path planning and tracking methods where the application of reinforcement learning is widespread. However, performance tuning and ensuring efficiency remains a significant open problem. Moreover, these methods suffer from a non-linearity issue of vehicle dynamics, that causes a deviation from the original route, and do not respect the BS ISO 16787-2017 standard that outlines the minimum requirements needed in APS. To overcome these limitations, our contribution in this paper, named DPPO-IL, is fourfold: (i) A new framework using the Proximal Policy optimization algorithm, allowing agent to explore an empty parking spot, plan then park a car in a random parking spot by avoiding static and dynamic obstacles; (ii) A dynamic adjustment of the reward function using intrinsic reward signals to induce the agent to explore more; (iii) An approach to learn policies from expert demonstrations using imitation learning combined with deep reinforcement learning to speed up the learning phase and reduce the training time; (iv) A task-specific curriculum learning to train the agent in a very complex environment. Experiments show promising results, especially that our approach managed to achieve a 90% success rate where 97% of them were aligned with the parking spot, with an inclination angle greater than ±0.2° and a deviation less than 0.1 meter. These results exceeded the state of the art while respecting the ISO 16787-2017 standard.
自动泊车系统(APS)负责以安全和高效的完全自主方式执行泊车机动。这些系统主要包括三种方法;车位探索，路径规划，路径跟踪。在文献中，有几种路径规划和跟踪方法，其中强化学习的应用非常广泛。然而，性能调优和确保效率仍然是一个悬而未决的重大问题。此外，这些方法受到车辆动力学非线性问题的影响，导致偏离原始路线，并且不符合BS ISO 16787-2017标准，该标准概述了APS所需的最低要求。为了克服这些限制，我们在本文中的贡献，命名为DPPO-IL，有四个方面:(i)使用近端策略优化算法的新框架，允许智能体探索一个空停车位，通过避开静态和动态障碍物，计划然后将汽车停放在随机停车位;(ii)利用内在奖励信号对奖励函数进行动态调整，诱导agent进行更多的探索;(iii)利用模仿学习结合深度强化学习从专家演示中学习政策的方法，以加快学习阶段并缩短训练时间;(iv)在非常复杂的环境中学习训练代理的特定任务课程。实验结果令人满意，特别是我们的方法达到了90%的成功率，其中97%的泊位与泊位对齐，倾角大于±0.2°，偏差小于0.1米。这些结果在遵守ISO 16787-2017标准的同时超越了最先进的水平。
{"title":"Dynamic Adjustment of Reward Function for Proximal Policy Optimization with Imitation Learning: Application to Automated Parking Systems","authors":"Mohamad Albilani, A. Bouzeghoub","doi":"10.1109/iv51971.2022.9827194","DOIUrl":"https://doi.org/10.1109/iv51971.2022.9827194","url":null,"abstract":"Automated Parking Systems (APS) are responsible for performing a parking maneuver in a secure and time-efficient full autonomy.These systems include mainly three methods; parking spot exploration, path planning, and path tracking. In the literature, there are several path planning and tracking methods where the application of reinforcement learning is widespread. However, performance tuning and ensuring efficiency remains a significant open problem. Moreover, these methods suffer from a non-linearity issue of vehicle dynamics, that causes a deviation from the original route, and do not respect the BS ISO 16787-2017 standard that outlines the minimum requirements needed in APS. To overcome these limitations, our contribution in this paper, named DPPO-IL, is fourfold: (i) A new framework using the Proximal Policy optimization algorithm, allowing agent to explore an empty parking spot, plan then park a car in a random parking spot by avoiding static and dynamic obstacles; (ii) A dynamic adjustment of the reward function using intrinsic reward signals to induce the agent to explore more; (iii) An approach to learn policies from expert demonstrations using imitation learning combined with deep reinforcement learning to speed up the learning phase and reduce the training time; (iv) A task-specific curriculum learning to train the agent in a very complex environment. Experiments show promising results, especially that our approach managed to achieve a 90% success rate where 97% of them were aligned with the parking spot, with an inclination angle greater than ±0.2° and a deviation less than 0.1 meter. These results exceeded the state of the art while respecting the ISO 16787-2017 standard.","PeriodicalId":184622,"journal":{"name":"2022 IEEE Intelligent Vehicles Symposium (IV)","volume":"22 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":"127625232","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/iv51971.2022.9827140
Matteo Bellusci, Matteo Matteucci
Cameras are among the most used sensors in Advanced Driver Assistance Systems (ADAS) and autonomous vehicles for their low cost and rich stream of information. Nevertheless, they require accurate extrinsic calibration to refer external features, e.g., obstacles and road line markings, to the vehicle reference frame. In this paper, we present a real-time online calibration procedure designed to adjust the camera’s pitch and height estimates by enforcing road line markings parallelism. Differently from most of the approaches in the literature, our is not limited to straight line markings as, under the assumption of local width constancy, parallelism is enforced also in case of high curvature line markings. Furthermore, to take into account the vehicle dynamics, e.g., accelerations and braking, our estimation procedure is framed in the context of an inverted pendulum dynamical system for which a robust filter is proposed. Finally, we experimentally assess the performance of the overall approach both in simulated and real scenarios.
{"title":"Advances in Real-Time Online Vehicle Camera Calibration via Road Line Markings Parallelism Enforcement*","authors":"Matteo Bellusci, Matteo Matteucci","doi":"10.1109/iv51971.2022.9827140","DOIUrl":"https://doi.org/10.1109/iv51971.2022.9827140","url":null,"abstract":"Cameras are among the most used sensors in Advanced Driver Assistance Systems (ADAS) and autonomous vehicles for their low cost and rich stream of information. Nevertheless, they require accurate extrinsic calibration to refer external features, e.g., obstacles and road line markings, to the vehicle reference frame. In this paper, we present a real-time online calibration procedure designed to adjust the camera’s pitch and height estimates by enforcing road line markings parallelism. Differently from most of the approaches in the literature, our is not limited to straight line markings as, under the assumption of local width constancy, parallelism is enforced also in case of high curvature line markings. Furthermore, to take into account the vehicle dynamics, e.g., accelerations and braking, our estimation procedure is framed in the context of an inverted pendulum dynamical system for which a robust filter is proposed. Finally, we experimentally assess the performance of the overall approach both in simulated and real scenarios.","PeriodicalId":184622,"journal":{"name":"2022 IEEE Intelligent Vehicles Symposium (IV)","volume":"58 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":"130652371","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}
Lane boundaries, as the main component of high definition maps (HD maps), are difficult to auto-generate accurately in various scenarios. In this paper, a general lane boundary extraction method is proposed for HD mapping in both highway and urban scenarios. Firstly, a learning-based heatmap regression network is applied to estimate the center of lane boundaries in bird’s eye view (BEV) images from light detection and ranging (LiDAR). Secondly, the geometry of various lane boundaries is extracted accurately in a coarse-to-fine strategy. Given the regression results, the geometry generation method initially extracts kinds of lane boundaries coarsely, including highway boundaries and complex cases in urban scenarios, such as splitting lane boundaries, lane boundaries in arbitrary directions, etc. Subsequently, the fine adjustment method increases the accuracy of the lane boundary geometry by inserting and adjusting the keypoints recursively according to the regression heatmap. To handle large-scale mapping, additional methods are presented to merge the same lane boundary including the connection priority strategy and adaptive lane vertex downsampling. Experiments demonstrate that the proposed method manages to generate accurate lane boundaries in both highway and urban scenarios with limited storage consumption, and therefore is an effective and storage-saving method for large-scale HD mapping.
{"title":"Coarse-to-Fine Lane Boundary Extraction for Large-Scale HD Mapping","authors":"Tianyi Li, Chuanbin Lai, Xun Chai, Lixia Shen, Yong Wu","doi":"10.1109/iv51971.2022.9827420","DOIUrl":"https://doi.org/10.1109/iv51971.2022.9827420","url":null,"abstract":"Lane boundaries, as the main component of high definition maps (HD maps), are difficult to auto-generate accurately in various scenarios. In this paper, a general lane boundary extraction method is proposed for HD mapping in both highway and urban scenarios. Firstly, a learning-based heatmap regression network is applied to estimate the center of lane boundaries in bird’s eye view (BEV) images from light detection and ranging (LiDAR). Secondly, the geometry of various lane boundaries is extracted accurately in a coarse-to-fine strategy. Given the regression results, the geometry generation method initially extracts kinds of lane boundaries coarsely, including highway boundaries and complex cases in urban scenarios, such as splitting lane boundaries, lane boundaries in arbitrary directions, etc. Subsequently, the fine adjustment method increases the accuracy of the lane boundary geometry by inserting and adjusting the keypoints recursively according to the regression heatmap. To handle large-scale mapping, additional methods are presented to merge the same lane boundary including the connection priority strategy and adaptive lane vertex downsampling. Experiments demonstrate that the proposed method manages to generate accurate lane boundaries in both highway and urban scenarios with limited storage consumption, and therefore is an effective and storage-saving method for large-scale HD mapping.","PeriodicalId":184622,"journal":{"name":"2022 IEEE Intelligent Vehicles Symposium (IV)","volume":"80 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":"130677594","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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