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/iv51971.2022.9827210
T. Fleischer, M. Puhe, J. Schippl, Yukari Yamasaki
Social acceptance is seen as an important prerequisite for a successful adoption and diffusion of automated driving technologies and services. The paper presents a proposal about how social acceptance could be conceptualized and argues that expectations and promises regarding the role of autonomous driving in future mobility systems should play an important role here. It then provides first results of a representative survey among German citizens, focusing on their individual expectations on the longer-term effects of the widespread use of autonomous road vehicles as well as on their opinions on changes of framework conditions and regulations associated with their diffusion and deployment.
{"title":"Public Expectations Regarding the Longer-term Implications of and Regulatory Changes for Autonomous Driving: A Contribution to the Debate on its Social Acceptance","authors":"T. Fleischer, M. Puhe, J. Schippl, Yukari Yamasaki","doi":"10.1109/iv51971.2022.9827210","DOIUrl":"https://doi.org/10.1109/iv51971.2022.9827210","url":null,"abstract":"Social acceptance is seen as an important prerequisite for a successful adoption and diffusion of automated driving technologies and services. The paper presents a proposal about how social acceptance could be conceptualized and argues that expectations and promises regarding the role of autonomous driving in future mobility systems should play an important role here. It then provides first results of a representative survey among German citizens, focusing on their individual expectations on the longer-term effects of the widespread use of autonomous road vehicles as well as on their opinions on changes of framework conditions and regulations associated with their diffusion and deployment.","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":"129109535","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/iv51971.2022.9827105
Zeyu Mu, Zheng Chen, Seunghan Ryu, S. Avedisov, Rui Guo, B. Park
In this paper, we showcase a framework for cooperative mixed traffic platooning that allows the platooning vehicles to realize multiple benefits from using vehicle-to-everything (V2X) communications and advanced controls on urban arterial roads. A mixed traffic platoon, in general, can be formulated by a lead and ego connected automated vehicles (CAVs) with one or more unconnected human-driven vehicles (UHVs) in between. As this platoon approaches an intersection, the lead vehicle uses signal phase and timing (SPaT) messages from the connected intersection to optimize its trajectory for travel time and energy efficiency as it passes through the intersection. These benefits carry over to the UHVs and the ego vehicle as they follow the lead vehicle. The ego vehicle then uses information from the lead vehicle received through basic safety messages (BSMs) to further optimize its safety, driving comfort, and energy consumption. This is accomplished by the recently designed cooperative adaptive cruise control with unconnected vehicles (CACCu). The performance benefits of our framework are proven and demonstrated by simulations using real-world platooning data from the CACC Field Operation Test (FOT) Dataset from the Netherlands.
{"title":"Cooperative Platooning with Mixed Traffic on Urban Arterial Roads","authors":"Zeyu Mu, Zheng Chen, Seunghan Ryu, S. Avedisov, Rui Guo, B. Park","doi":"10.1109/iv51971.2022.9827105","DOIUrl":"https://doi.org/10.1109/iv51971.2022.9827105","url":null,"abstract":"In this paper, we showcase a framework for cooperative mixed traffic platooning that allows the platooning vehicles to realize multiple benefits from using vehicle-to-everything (V2X) communications and advanced controls on urban arterial roads. A mixed traffic platoon, in general, can be formulated by a lead and ego connected automated vehicles (CAVs) with one or more unconnected human-driven vehicles (UHVs) in between. As this platoon approaches an intersection, the lead vehicle uses signal phase and timing (SPaT) messages from the connected intersection to optimize its trajectory for travel time and energy efficiency as it passes through the intersection. These benefits carry over to the UHVs and the ego vehicle as they follow the lead vehicle. The ego vehicle then uses information from the lead vehicle received through basic safety messages (BSMs) to further optimize its safety, driving comfort, and energy consumption. This is accomplished by the recently designed cooperative adaptive cruise control with unconnected vehicles (CACCu). The performance benefits of our framework are proven and demonstrated by simulations using real-world platooning data from the CACC Field Operation Test (FOT) Dataset from the Netherlands.","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":"133611706","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}
Reasonable power distribution between battery and supercapacitor in electric vehicles is a crucial problem to improve energy consumption and economy. An online energy management strategy based on model predictive control (MPC) is proposed in this paper. Firstly, a radial basis function neural network optimized by particle swarm algorithm is presented to generate the short-term future velocity, i.e., the reference trajectory of the MPC. Then, a cost function considering the battery degradation cost and the electricity cost is constructed and optimized within each prediction horizon while maintaining the state of charge of the supercapacitor. Simulation results on the UDDS driving cycle show that the total cost of the proposed strategy is reduced by 6.3% and 3.9% compared with the near-optimal rule-based strategy and the none optimized velocity predictor-MPC, respectively, indicating that the velocity prediction accuracy has a significant impact on the performance of real-time energy management.
{"title":"Energy Management Strategy for Hybrid Energy Storage System using Optimized Velocity Predictor and Model Predictive Control","authors":"Zhiwu Huang, Pei Huang, Yue Wu, Heng Li, Hui Peng, Jun Peng","doi":"10.1109/iv51971.2022.9827322","DOIUrl":"https://doi.org/10.1109/iv51971.2022.9827322","url":null,"abstract":"Reasonable power distribution between battery and supercapacitor in electric vehicles is a crucial problem to improve energy consumption and economy. An online energy management strategy based on model predictive control (MPC) is proposed in this paper. Firstly, a radial basis function neural network optimized by particle swarm algorithm is presented to generate the short-term future velocity, i.e., the reference trajectory of the MPC. Then, a cost function considering the battery degradation cost and the electricity cost is constructed and optimized within each prediction horizon while maintaining the state of charge of the supercapacitor. Simulation results on the UDDS driving cycle show that the total cost of the proposed strategy is reduced by 6.3% and 3.9% compared with the near-optimal rule-based strategy and the none optimized velocity predictor-MPC, respectively, indicating that the velocity prediction accuracy has a significant impact on the performance of real-time energy management.","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":"115934452","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}
Pub Date : 2022-06-05DOI: 10.1109/iv51971.2022.9827145
Hans-Joachim Bieg, Simon Strobel, M. Fischer, Paula Laßmann
Methods to estimate a driver’s visual attention from video images have received increased research interest. Such methods are especially important for detecting inattentive drivers in partially automated vehicles. The current study compares different driver gaze region estimation techniques, which may serve as a basis for detecting inattentive drivers. The accuracy of these techniques was evaluated on data from automated drives in a driving simulator. The examined techniques include a classical, state-of-the-art eye tracking approach, two data-driven approaches that rely on eye tracking data, a data-driven approach that only considers the driver’s facial configuration, and an end-to-end approach based on a convolutional neural network. The results showcase the advantages of data-driven approaches over a classical geometric interpretation of the eye tracking data. The results also highlight challenges regarding generalization for purely data-driven approaches and the benefits of data-driven approaches that operate on eye tracking data rather than video image data alone.
{"title":"Comparison of Video-based Driver Gaze Region Estimation Techniques","authors":"Hans-Joachim Bieg, Simon Strobel, M. Fischer, Paula Laßmann","doi":"10.1109/iv51971.2022.9827145","DOIUrl":"https://doi.org/10.1109/iv51971.2022.9827145","url":null,"abstract":"Methods to estimate a driver’s visual attention from video images have received increased research interest. Such methods are especially important for detecting inattentive drivers in partially automated vehicles. The current study compares different driver gaze region estimation techniques, which may serve as a basis for detecting inattentive drivers. The accuracy of these techniques was evaluated on data from automated drives in a driving simulator. The examined techniques include a classical, state-of-the-art eye tracking approach, two data-driven approaches that rely on eye tracking data, a data-driven approach that only considers the driver’s facial configuration, and an end-to-end approach based on a convolutional neural network. The results showcase the advantages of data-driven approaches over a classical geometric interpretation of the eye tracking data. The results also highlight challenges regarding generalization for purely data-driven approaches and the benefits of data-driven approaches that operate on eye tracking data rather than video image data alone.","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":"130888006","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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