Pub Date : 2022-06-05DOI: 10.1109/iv51971.2022.9827454
A. Hekimoglu, Michael Schmidt, Alvaro Marcos-Ramiro, G. Rigoll
Processing camera information to perceive their 3D surrounding is essential for building scalable autonomous driving vehicles. For this task, deep learning networks provide effective real-time solutions. However, to compensate for missing depth information in cameras compared to LiDARs, a large amount of labeled data is required for training. Active learning is a training framework where the network actively participates in the data selection process to improve data efficiency and performance. In this work, we propose an active learning pipeline for 3D object detection from monocular images. The main components of our approach are (1) two training-efficient uncertainty estimation strategies, (2) a diversity-based selection strategy to select images that contain the most diverse set of objects, (3) a novel active learning strategy more suitable for training autonomous driving perception networks. Experiments show that combining our proposed uncertainty estimation methods provides a better data saving rate and reaches a higher final performance than baselines. Furthermore, we empirically show performance gains of the presented diversity-based selection strategy and the efficiency of the proposed active learning strategy.
{"title":"Efficient Active Learning Strategies for Monocular 3D Object Detection","authors":"A. Hekimoglu, Michael Schmidt, Alvaro Marcos-Ramiro, G. Rigoll","doi":"10.1109/iv51971.2022.9827454","DOIUrl":"https://doi.org/10.1109/iv51971.2022.9827454","url":null,"abstract":"Processing camera information to perceive their 3D surrounding is essential for building scalable autonomous driving vehicles. For this task, deep learning networks provide effective real-time solutions. However, to compensate for missing depth information in cameras compared to LiDARs, a large amount of labeled data is required for training. Active learning is a training framework where the network actively participates in the data selection process to improve data efficiency and performance. In this work, we propose an active learning pipeline for 3D object detection from monocular images. The main components of our approach are (1) two training-efficient uncertainty estimation strategies, (2) a diversity-based selection strategy to select images that contain the most diverse set of objects, (3) a novel active learning strategy more suitable for training autonomous driving perception networks. Experiments show that combining our proposed uncertainty estimation methods provides a better data saving rate and reaches a higher final performance than baselines. Furthermore, we empirically show performance gains of the presented diversity-based selection strategy and the efficiency of the proposed active learning strategy.","PeriodicalId":184622,"journal":{"name":"2022 IEEE Intelligent Vehicles Symposium (IV)","volume":"108 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":"125186001","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/iv51971.2022.9827198
Silvia Thal, R. Henze, Ryo Hasegawa, H. Nakamura, H. Imanaga, J. Antona-Makoshi, N. Uchida
This study enhances automated driving scenario-based safety assessment methods previously developed for highways, and enables their application to urban areas. First, we propose a methodology for matching open source map data with naturalistic driving data recorded with test vehicles. The methodology proposed proved feasible detecting various geometry-related scenarios and can contribute to overcome the difficulties to create representative real driving urban scenario databases that cover such geometries. Second, a search-based test case generation methodology previously developed to fulfill requirements of severity, exposure and realism with a focus on highways, is further developed and adapted to active urban scenarios. Active scenarios require an active maneuver decision of the Vehicle under Test and have not been considered in related work so far. To show the feasibility of the methodologies proposed, we apply them to a set of Left Turn Across Path / Opposite Direction scenarios, extracted from an existing urban driving database. The map matching and the search-based test case generation methodology succeeded in deriving test cases, which equally account for exposure and coverage criteria for normal driving situations in urban settings.
{"title":"Generic Detection and Search-based Test Case Generation of Urban Scenarios based on Real Driving Data","authors":"Silvia Thal, R. Henze, Ryo Hasegawa, H. Nakamura, H. Imanaga, J. Antona-Makoshi, N. Uchida","doi":"10.1109/iv51971.2022.9827198","DOIUrl":"https://doi.org/10.1109/iv51971.2022.9827198","url":null,"abstract":"This study enhances automated driving scenario-based safety assessment methods previously developed for highways, and enables their application to urban areas. First, we propose a methodology for matching open source map data with naturalistic driving data recorded with test vehicles. The methodology proposed proved feasible detecting various geometry-related scenarios and can contribute to overcome the difficulties to create representative real driving urban scenario databases that cover such geometries. Second, a search-based test case generation methodology previously developed to fulfill requirements of severity, exposure and realism with a focus on highways, is further developed and adapted to active urban scenarios. Active scenarios require an active maneuver decision of the Vehicle under Test and have not been considered in related work so far. To show the feasibility of the methodologies proposed, we apply them to a set of Left Turn Across Path / Opposite Direction scenarios, extracted from an existing urban driving database. The map matching and the search-based test case generation methodology succeeded in deriving test cases, which equally account for exposure and coverage criteria for normal driving situations in urban settings.","PeriodicalId":184622,"journal":{"name":"2022 IEEE Intelligent Vehicles Symposium (IV)","volume":"112 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":"122883298","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/iv51971.2022.9827015
He Zhang, Huajun Zhou, Jian Sun, Ye Tian
One essential goal for Highly Automated Vehicles (HAVs) safety test is to assess their risk rate in naturalistic driving environment, and to compare their performance with human drivers. The probability of exposure to risk events is generally low, making the test process extremely time-consuming. To address this, we proposed a surrogate-based method in scenario-based simulation test to expediate the assessment of the risk rate of HAVs. HighD data were used to fit the naturalistic distribution and to estimate the probability of each concrete scenario. Machine learning model-based surrogates were proposed to quickly approximate the test result of each concrete scenario. Considering the different capabilities and domains of various surrogate models, we applied six surrogate models to search for two types of targeted scenarios with different risk levels and rarity levels. We proved that the performances of different surrogate models greatly distinguish from each other when the target scenarios are extremely rare. Inverse Distance Weighted (IDW) was the most efficient surrogate model, which could achieve risk rate assessment with only 2.5% test resources. The required CPU runtime of IDW was 2% of that required by Kriging. The proposed method has great potential in accelerating the risk assessment of HAVs.
{"title":"Risk Assessment of Highly Automated Vehicles with Naturalistic Driving Data: A Surrogate-based optimization Method","authors":"He Zhang, Huajun Zhou, Jian Sun, Ye Tian","doi":"10.1109/iv51971.2022.9827015","DOIUrl":"https://doi.org/10.1109/iv51971.2022.9827015","url":null,"abstract":"One essential goal for Highly Automated Vehicles (HAVs) safety test is to assess their risk rate in naturalistic driving environment, and to compare their performance with human drivers. The probability of exposure to risk events is generally low, making the test process extremely time-consuming. To address this, we proposed a surrogate-based method in scenario-based simulation test to expediate the assessment of the risk rate of HAVs. HighD data were used to fit the naturalistic distribution and to estimate the probability of each concrete scenario. Machine learning model-based surrogates were proposed to quickly approximate the test result of each concrete scenario. Considering the different capabilities and domains of various surrogate models, we applied six surrogate models to search for two types of targeted scenarios with different risk levels and rarity levels. We proved that the performances of different surrogate models greatly distinguish from each other when the target scenarios are extremely rare. Inverse Distance Weighted (IDW) was the most efficient surrogate model, which could achieve risk rate assessment with only 2.5% test resources. The required CPU runtime of IDW was 2% of that required by Kriging. The proposed method has great potential in accelerating the risk assessment of HAVs.","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":"130185068","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/iv51971.2022.9827296
Sadullah Goncu, Ismet Göksad Erdagi, Mehmet Ali Silgu, H. B. Çelikoglu
Car-following (CF) behavior is the most abstract form of driving action and, CF behavior modeling has been one of the core aspects of traffic engineering studies for several decades. The literature about CF behavior modeling is vibrant and still evolving. Furthermore, the effect of CF models on the traffic flow performance through case studies on different traffic facilities is still being investigated. To shed light on this matter, this study presents a microsimulation-based case study considering a freeway stretch in Istanbul, Turkey, employing two different CF models, i.e., Intelligent Driver Model (IDM) and Wiedemann 99 through scenarios. Simulation of Urban Mobility (SUMO) is utilized as the microsimulation environment. Both CF models are calibrated according to the measurements. Scenarios for the comparative evaluation are setup based on the questions “What if German drivers used this freeway stretch? How much would the traffic flow performance change?" Using different case studies conducted in German Freeways on the literature, simulation model parameters are obtained for both models and, simulation analyses are performed. Traffic flow performances are evaluated based on the selected performance measures, such as throughput and total travel time. According to the findings, it is seen that results differ significantly between scenarios. We elaborate on the differences obtained and discuss the implications on different scenarios which are handled through different CF models.
{"title":"Analysis on Effects of Driving Behavior on Freeway Traffic Flow: A Comparative Evaluation of Two Driver Profiles Using Two Car-Following Models","authors":"Sadullah Goncu, Ismet Göksad Erdagi, Mehmet Ali Silgu, H. B. Çelikoglu","doi":"10.1109/iv51971.2022.9827296","DOIUrl":"https://doi.org/10.1109/iv51971.2022.9827296","url":null,"abstract":"Car-following (CF) behavior is the most abstract form of driving action and, CF behavior modeling has been one of the core aspects of traffic engineering studies for several decades. The literature about CF behavior modeling is vibrant and still evolving. Furthermore, the effect of CF models on the traffic flow performance through case studies on different traffic facilities is still being investigated. To shed light on this matter, this study presents a microsimulation-based case study considering a freeway stretch in Istanbul, Turkey, employing two different CF models, i.e., Intelligent Driver Model (IDM) and Wiedemann 99 through scenarios. Simulation of Urban Mobility (SUMO) is utilized as the microsimulation environment. Both CF models are calibrated according to the measurements. Scenarios for the comparative evaluation are setup based on the questions “What if German drivers used this freeway stretch? How much would the traffic flow performance change?\" Using different case studies conducted in German Freeways on the literature, simulation model parameters are obtained for both models and, simulation analyses are performed. Traffic flow performances are evaluated based on the selected performance measures, such as throughput and total travel time. According to the findings, it is seen that results differ significantly between scenarios. We elaborate on the differences obtained and discuss the implications on different scenarios which are handled through different CF models.","PeriodicalId":184622,"journal":{"name":"2022 IEEE Intelligent Vehicles Symposium (IV)","volume":"26 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":"133794108","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/iv51971.2022.9827022
Hao Yang, Y. Farid, K. Oguchi
Vehicle incidents or anomalous slow/stopping vehicles will generate non-recurrent queues and partially block roads. The queues will result in unbalanced lane-level traffic, and the large speed differences among lanes increase the difficulty for the queued vehicles to make lane changes to avoid downstream congestion. In this paper, a centralized non-recurrent queue management (C-NRQM) system is implemented to assist connected vehicles around non-recurrent queues with advisory speed and lane changing instructions to mitigate road congestion as well as to minimize the travel time delay and risk of collisions of all vehicles. A systematic evaluation of the system is conducted with microscopic traffic simulations to assess its mobility and safety benefits under different market penetration rates (MPRs) of connected vehicles. The socially responsibility of the system on the fairness of all road users and its performance under a competing environment with different connected vehicle applications are also evaluated to illustrate its real-world implementations in the future transportation systems. The system can reduces travel time delay by more than 80% for road with medium congestion, and more than 50% for more congested roads. Also, the system evaluation demonstrates that the centralized management has a distinct advantage on improving network performance at high MPRs of connected vehicles and eliminating the negative impact of the competition of different mobility services
{"title":"Systematic Evaluation of A Centralized Non-Recurrent Queue Management System","authors":"Hao Yang, Y. Farid, K. Oguchi","doi":"10.1109/iv51971.2022.9827022","DOIUrl":"https://doi.org/10.1109/iv51971.2022.9827022","url":null,"abstract":"Vehicle incidents or anomalous slow/stopping vehicles will generate non-recurrent queues and partially block roads. The queues will result in unbalanced lane-level traffic, and the large speed differences among lanes increase the difficulty for the queued vehicles to make lane changes to avoid downstream congestion. In this paper, a centralized non-recurrent queue management (C-NRQM) system is implemented to assist connected vehicles around non-recurrent queues with advisory speed and lane changing instructions to mitigate road congestion as well as to minimize the travel time delay and risk of collisions of all vehicles. A systematic evaluation of the system is conducted with microscopic traffic simulations to assess its mobility and safety benefits under different market penetration rates (MPRs) of connected vehicles. The socially responsibility of the system on the fairness of all road users and its performance under a competing environment with different connected vehicle applications are also evaluated to illustrate its real-world implementations in the future transportation systems. The system can reduces travel time delay by more than 80% for road with medium congestion, and more than 50% for more congested roads. Also, the system evaluation demonstrates that the centralized management has a distinct advantage on improving network performance at high MPRs of connected vehicles and eliminating the negative impact of the competition of different mobility services","PeriodicalId":184622,"journal":{"name":"2022 IEEE Intelligent Vehicles Symposium (IV)","volume":"234 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":"114260728","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/iv51971.2022.9827372
Sai Krishna Kaushik Karanam, Thibaud Duhautbout, R. Talj, V. Berge-Cherfaoui, F. Aioun, F. Guillemard
Path planning algorithms for autonomous vehicles need to account for safety and comfort, more so, in scenarios where the possibility of casualties are higher due to increased traffic frequency and limited visibility. In this paper, we discuss the idea of a virtual obstacle deployed at occluded scenarios to avoid a potential collision or severe deceleration of the ego-vehicle. Urban scenarios like intersections, roundabout and merging are experimented. Results of simulating the integration of virtual obstacle with the trajectory planning algorithm, are analyzed in detail comparing speed and acceleration profiles.
{"title":"Virtual Obstacle for a Safe and Comfortable Approach to Limited Visibility Situations in Urban Autonomous Driving","authors":"Sai Krishna Kaushik Karanam, Thibaud Duhautbout, R. Talj, V. Berge-Cherfaoui, F. Aioun, F. Guillemard","doi":"10.1109/iv51971.2022.9827372","DOIUrl":"https://doi.org/10.1109/iv51971.2022.9827372","url":null,"abstract":"Path planning algorithms for autonomous vehicles need to account for safety and comfort, more so, in scenarios where the possibility of casualties are higher due to increased traffic frequency and limited visibility. In this paper, we discuss the idea of a virtual obstacle deployed at occluded scenarios to avoid a potential collision or severe deceleration of the ego-vehicle. Urban scenarios like intersections, roundabout and merging are experimented. Results of simulating the integration of virtual obstacle with the trajectory planning algorithm, are analyzed in detail comparing speed and acceleration profiles.","PeriodicalId":184622,"journal":{"name":"2022 IEEE Intelligent Vehicles Symposium (IV)","volume":"123 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":"116718436","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/iv51971.2022.9827191
A. Abdo, Guoyuan Wu, Qi Zhu, Nael B. Abu-Ghazaleh
Connected vehicle (CV) applications promise to revolutionize our transportation systems, improving safety and traffic capacity while reducing environmental footprint. Many CV applications have been proposed towards these goals, with the US Department of Transportation (USDOT) recently initiating some designated deployment sites to enable experimentation and validation. While the focus of this initial development effort is on demonstrating the functionality of a range of proposed applications, recent attacks have demonstrated their vulnerability to application level attacks. In these attacks, a malicious actor operates within the application’s parameters but providing falsified information. This paper explores a framework that protects against such application-level attacks. Then, we analyze the impact of the attacks, showing that an individual attacker can have substantial effects on the safety and efficiency of traffic flow even in the presence of message security standards developed by USDOT, motivating the need for our defense. Our defense relies on physically modeling the vehicles and their interaction using dynamic models and state estimation filters as well as reinforcement learning. It combines these observations with knowledge of application rules and guidelines to capture logic deviations. We demonstrate that the resultant defense, called CVGuard, can accurately and promptly detect attacks, with low false positive rates over a range of attack scenarios for different CV applications.
{"title":"CVGuard: Mitigating Application Attacks on Connected Vehicles","authors":"A. Abdo, Guoyuan Wu, Qi Zhu, Nael B. Abu-Ghazaleh","doi":"10.1109/iv51971.2022.9827191","DOIUrl":"https://doi.org/10.1109/iv51971.2022.9827191","url":null,"abstract":"Connected vehicle (CV) applications promise to revolutionize our transportation systems, improving safety and traffic capacity while reducing environmental footprint. Many CV applications have been proposed towards these goals, with the US Department of Transportation (USDOT) recently initiating some designated deployment sites to enable experimentation and validation. While the focus of this initial development effort is on demonstrating the functionality of a range of proposed applications, recent attacks have demonstrated their vulnerability to application level attacks. In these attacks, a malicious actor operates within the application’s parameters but providing falsified information. This paper explores a framework that protects against such application-level attacks. Then, we analyze the impact of the attacks, showing that an individual attacker can have substantial effects on the safety and efficiency of traffic flow even in the presence of message security standards developed by USDOT, motivating the need for our defense. Our defense relies on physically modeling the vehicles and their interaction using dynamic models and state estimation filters as well as reinforcement learning. It combines these observations with knowledge of application rules and guidelines to capture logic deviations. We demonstrate that the resultant defense, called CVGuard, can accurately and promptly detect attacks, with low false positive rates over a range of attack scenarios for different CV applications.","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":"130035055","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/iv51971.2022.9827381
Lie Guo, Liang Huang, Yibing Zhao
In LiDAR-based point cloud, objects are always represented as 3D bounding boxes with direction. LiDAR-based object detection task is similar to image-based task but comes with additional challenges. In LiDAR-based detection for autonomous vehicles, the size of 3D object is significant smaller compared with size of input scene represented by point cloud, thus conventional 3D backbones cannot effectively preserve detail geometric information of object with only a few points. To resolve this problem, this paper presents a MBConv Submanifold module, which is simple and effective for voxel-based detector from point cloud. The novel convolution architecture introduces inverted bottleneck and residual connection into 3D sparse backbone, which enable detector to learn high dimension feature from point cloud. Experiments shows that MBConv Submanifold module bring consistent improvement over the baseline method: MBConv Submanifold achieves the AP of 68.03% and 54.74% in the moderate cyclist and pedestrian category on the KITTI validation benchmark, surpass the baseline method significantly. Our code and pretrained models are available at: https://github.com/s1mpleee/ResMBSubmanifold.
{"title":"Residual MBConv Submanifold Module for 3D LiDAR-based Object Detection","authors":"Lie Guo, Liang Huang, Yibing Zhao","doi":"10.1109/iv51971.2022.9827381","DOIUrl":"https://doi.org/10.1109/iv51971.2022.9827381","url":null,"abstract":"In LiDAR-based point cloud, objects are always represented as 3D bounding boxes with direction. LiDAR-based object detection task is similar to image-based task but comes with additional challenges. In LiDAR-based detection for autonomous vehicles, the size of 3D object is significant smaller compared with size of input scene represented by point cloud, thus conventional 3D backbones cannot effectively preserve detail geometric information of object with only a few points. To resolve this problem, this paper presents a MBConv Submanifold module, which is simple and effective for voxel-based detector from point cloud. The novel convolution architecture introduces inverted bottleneck and residual connection into 3D sparse backbone, which enable detector to learn high dimension feature from point cloud. Experiments shows that MBConv Submanifold module bring consistent improvement over the baseline method: MBConv Submanifold achieves the AP of 68.03% and 54.74% in the moderate cyclist and pedestrian category on the KITTI validation benchmark, surpass the baseline method significantly. Our code and pretrained models are available at: https://github.com/s1mpleee/ResMBSubmanifold.","PeriodicalId":184622,"journal":{"name":"2022 IEEE Intelligent Vehicles Symposium (IV)","volume":"9 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":"130238802","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/iv51971.2022.9827297
Alejandro Diaz-Diaz, M. Ocaña, A. Llamazares, Carlos Gómez Huélamo, P. Revenga, L. Bergasa
Autonomous vehicle (AV) is one of the most challenging engineering tasks of our era. High-Definition (HD) maps are a fundamental tool in the development of AVs, being considered as pseudo sensors that provide a trusted baseline that other sensors cannot. Our approach is focused on the use of OpenDRIVE standard based HD maps in order to conduct the different mapping and planning tasks involved in Autonomous Driving (AD). In this paper we present a method for exploiting the HD map potential for two specific purposes: i) Global Path Planning and ii) Monitoring the relevant lanes and regulatory elements around the ego-vehicle to support the perception module. Mapping and planning modules are connected to the other modules of the AV stack by using ROS (Robot Operating System). Our AD architecture has been validated both in local and CARLA Autonomous Driving Leaderboard cloud, where we can appreciate a considerable improvement in the metrics by incorporating information from the HD map, not only used to conduct the Global Path Planning task but also providing prior information to the Perception module. Code is available in https://github.com/AlejandroDiazD/opendrive-mapping-planning.
{"title":"HD maps: Exploiting OpenDRIVE potential for Path Planning and Map Monitoring","authors":"Alejandro Diaz-Diaz, M. Ocaña, A. Llamazares, Carlos Gómez Huélamo, P. Revenga, L. Bergasa","doi":"10.1109/iv51971.2022.9827297","DOIUrl":"https://doi.org/10.1109/iv51971.2022.9827297","url":null,"abstract":"Autonomous vehicle (AV) is one of the most challenging engineering tasks of our era. High-Definition (HD) maps are a fundamental tool in the development of AVs, being considered as pseudo sensors that provide a trusted baseline that other sensors cannot. Our approach is focused on the use of OpenDRIVE standard based HD maps in order to conduct the different mapping and planning tasks involved in Autonomous Driving (AD). In this paper we present a method for exploiting the HD map potential for two specific purposes: i) Global Path Planning and ii) Monitoring the relevant lanes and regulatory elements around the ego-vehicle to support the perception module. Mapping and planning modules are connected to the other modules of the AV stack by using ROS (Robot Operating System). Our AD architecture has been validated both in local and CARLA Autonomous Driving Leaderboard cloud, where we can appreciate a considerable improvement in the metrics by incorporating information from the HD map, not only used to conduct the Global Path Planning task but also providing prior information to the Perception module. Code is available in https://github.com/AlejandroDiazD/opendrive-mapping-planning.","PeriodicalId":184622,"journal":{"name":"2022 IEEE Intelligent Vehicles Symposium (IV)","volume":"7 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":"129299029","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/iv51971.2022.9827367
Tsuyoshi Goto, Hidenori Itaya, Tsubasa Hirakawa, Takayoshi Yamashita, H. Fujiyoshi
Autonomous driving system controls a vehicle using path planning. Path planning for automated vehicles observes a vehicle and the surrounding information and plans a trajectory on the basis of rule-based approach. However, the rule-based path planning cannot generate an appropriate trajectory for complex scenes, such as two vehicles passes each other at an intersection without traffic lights. Such complex scene is called deadlock. For avoiding the deadlock, it is very costly to create rules manually. In this paper, we propose a multi-agent deep reinforcement learning method to generate appropriate trajectories at the deadlock scenes. The proposed method consists of a single feature extractor and actor-critic branches. Moreover, we introduce a mask-attention mechanism for visual explanation. By taking a look at the obtained attention maps, we can confirm the obtained agent and the reason of the behavior. For evaluating our method, we develop a simulator environment of autonomous driving that produces a certain deadlock scene. The experimental results with the developed environment show that the proposed method can generate trajectories avoiding deadlocks.
{"title":"Solving the Deadlock Problem with Deep Reinforcement Learning Using Information from Multiple Vehicles","authors":"Tsuyoshi Goto, Hidenori Itaya, Tsubasa Hirakawa, Takayoshi Yamashita, H. Fujiyoshi","doi":"10.1109/iv51971.2022.9827367","DOIUrl":"https://doi.org/10.1109/iv51971.2022.9827367","url":null,"abstract":"Autonomous driving system controls a vehicle using path planning. Path planning for automated vehicles observes a vehicle and the surrounding information and plans a trajectory on the basis of rule-based approach. However, the rule-based path planning cannot generate an appropriate trajectory for complex scenes, such as two vehicles passes each other at an intersection without traffic lights. Such complex scene is called deadlock. For avoiding the deadlock, it is very costly to create rules manually. In this paper, we propose a multi-agent deep reinforcement learning method to generate appropriate trajectories at the deadlock scenes. The proposed method consists of a single feature extractor and actor-critic branches. Moreover, we introduce a mask-attention mechanism for visual explanation. By taking a look at the obtained attention maps, we can confirm the obtained agent and the reason of the behavior. For evaluating our method, we develop a simulator environment of autonomous driving that produces a certain deadlock scene. The experimental results with the developed environment show that the proposed method can generate trajectories avoiding deadlocks.","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":"129348799","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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