Pub Date : 2022-06-05DOI: 10.1109/iv51971.2022.9827256
Rohini Poolat Parameswarath, B. Sikdar
Modern cars come with Keyless Entry Systems that can be either Remote Keyless Entry (RKE) systems or Passive Keyless Entry and Start (PKES) systems. In the initial versions of RKE implementation, fixed code was used by the key fob to unlock the car door. However, this method is vulnerable to replay attacks as an adversary may capture and replay the same code later to unlock the car. A rolling code system was introduced to protect RKE systems from such replay attacks. Studies have shown that even the rolling code system is vulnerable to certain attacks. In this work, we investigate the attacks possible on RKE systems and propose an efficient and effective authentication mechanism to defend RKE systems against such attacks with minimal changes to the existing RKE system. The proposed mechanism makes use of hashing and asymmetric cryptographic techniques for the secure transmission of signals from the key fob to the car that cannot be replayed. The security of the proposed mechanism is shown using informal security proof and simulation of the proposed solution is also provided.
{"title":"An Authentication Mechanism for Remote Keyless Entry Systems in Cars to Prevent Replay and RollJam Attacks","authors":"Rohini Poolat Parameswarath, B. Sikdar","doi":"10.1109/iv51971.2022.9827256","DOIUrl":"https://doi.org/10.1109/iv51971.2022.9827256","url":null,"abstract":"Modern cars come with Keyless Entry Systems that can be either Remote Keyless Entry (RKE) systems or Passive Keyless Entry and Start (PKES) systems. In the initial versions of RKE implementation, fixed code was used by the key fob to unlock the car door. However, this method is vulnerable to replay attacks as an adversary may capture and replay the same code later to unlock the car. A rolling code system was introduced to protect RKE systems from such replay attacks. Studies have shown that even the rolling code system is vulnerable to certain attacks. In this work, we investigate the attacks possible on RKE systems and propose an efficient and effective authentication mechanism to defend RKE systems against such attacks with minimal changes to the existing RKE system. The proposed mechanism makes use of hashing and asymmetric cryptographic techniques for the secure transmission of signals from the key fob to the car that cannot be replayed. The security of the proposed mechanism is shown using informal security proof and simulation of the proposed solution is also provided.","PeriodicalId":184622,"journal":{"name":"2022 IEEE Intelligent Vehicles Symposium (IV)","volume":"28 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":"128905624","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/iv51971.2022.9827255
Magnus Gyllenhammar, G. R. Campos, Fredrik Sandblom, Martin Törngren, H. Sivencrona
Ensuring safety is arguably one of the largest remaining challenges before wide-spread market adoption of Automated Driving Systems (ADSs). One central aspect is how to provide evidence for the fulfilment of the safety claims and, in particular, how to produce a predictive and reliable safety case considering both the absence and the presence of faults in the system. In order to provide such evidence, there is a need for describing and modelling the different elements of the ADS and its operational context: models of event exposure, sensing and perception models, as well as actuation and closed-loop behaviour representations. This paper explores how estimates from such statistical models can impact the performance and operation of an ADS and, in particular, how such models can be continuously improved by incorporating more field data retrieved during the operation of (previous versions 00 the ADS. Focusing on the safe driving velocity, this results in the ability to update the driving policy so to maximise the allowed safe velocity, for which the safety claim still holds. For illustration purposes, an example considering statistical models of the exposure to an adverse event, as well as failures related to the system’s perception system, is analysed. Estimations from these models, using statistical confidence limits, are used to derive a safe driving policy of the ADS. The results highlight the importance of leveraging field data in order to improve the system’s abilities and performance, while remaining safe. The proposed methodology, leveraging a data-driven approach, also shows how the system’s safety can be monitored and maintained, while allowing for incremental expansion and improvements of the ADS.
{"title":"Uncertainty Aware Data Driven Precautionary Safety for Automated Driving Systems Considering Perception Failures and Event Exposure","authors":"Magnus Gyllenhammar, G. R. Campos, Fredrik Sandblom, Martin Törngren, H. Sivencrona","doi":"10.1109/iv51971.2022.9827255","DOIUrl":"https://doi.org/10.1109/iv51971.2022.9827255","url":null,"abstract":"Ensuring safety is arguably one of the largest remaining challenges before wide-spread market adoption of Automated Driving Systems (ADSs). One central aspect is how to provide evidence for the fulfilment of the safety claims and, in particular, how to produce a predictive and reliable safety case considering both the absence and the presence of faults in the system. In order to provide such evidence, there is a need for describing and modelling the different elements of the ADS and its operational context: models of event exposure, sensing and perception models, as well as actuation and closed-loop behaviour representations. This paper explores how estimates from such statistical models can impact the performance and operation of an ADS and, in particular, how such models can be continuously improved by incorporating more field data retrieved during the operation of (previous versions 00 the ADS. Focusing on the safe driving velocity, this results in the ability to update the driving policy so to maximise the allowed safe velocity, for which the safety claim still holds. For illustration purposes, an example considering statistical models of the exposure to an adverse event, as well as failures related to the system’s perception system, is analysed. Estimations from these models, using statistical confidence limits, are used to derive a safe driving policy of the ADS. The results highlight the importance of leveraging field data in order to improve the system’s abilities and performance, while remaining safe. The proposed methodology, leveraging a data-driven approach, also shows how the system’s safety can be monitored and maintained, while allowing for incremental expansion and improvements of the ADS.","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":"130495988","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/iv51971.2022.9827148
Hao-Jan Ke, Saeed Mozaffari, S. Alirezaee, M. Saif
In this paper, a cooperative adaptive cruise control (CACC) system is presented with integrated lidar and vehicle-to-vehicle (V2V) communication. Firstly, an adaptive cruise control system (ACC) is designed for the Q-Car electrical vehicle, an autonomous car. Secondly, a CACC system and V2V communication are designed based on a new algorithm to improve the ACC system performance. Lastly, the CACC agent was trained by Deep Q learning (DQN) and tested. The proposed CACC system improved the stability of the vehicle. Experimental results demonstrate that the CACC system can decrease the average inter-vehicular distance of ACC by 44.74%, with an additional 40.19% when DQN was utilized. The vehicles communicate with each other through a WiFi module to transmit information with 1ms latency.
{"title":"Cooperative Adaptive Cruise Control using Vehicle-to-Vehicle communication and Deep Learning","authors":"Hao-Jan Ke, Saeed Mozaffari, S. Alirezaee, M. Saif","doi":"10.1109/iv51971.2022.9827148","DOIUrl":"https://doi.org/10.1109/iv51971.2022.9827148","url":null,"abstract":"In this paper, a cooperative adaptive cruise control (CACC) system is presented with integrated lidar and vehicle-to-vehicle (V2V) communication. Firstly, an adaptive cruise control system (ACC) is designed for the Q-Car electrical vehicle, an autonomous car. Secondly, a CACC system and V2V communication are designed based on a new algorithm to improve the ACC system performance. Lastly, the CACC agent was trained by Deep Q learning (DQN) and tested. The proposed CACC system improved the stability of the vehicle. Experimental results demonstrate that the CACC system can decrease the average inter-vehicular distance of ACC by 44.74%, with an additional 40.19% when DQN was utilized. The vehicles communicate with each other through a WiFi module to transmit information with 1ms latency.","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":"131341858","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/iv51971.2022.9827214
Bianca Forkel, Hans-Joachim Wünsche
For autonomous driving on rural or dirt roads-neither urban nor off-road - a large terrain area needs to be estimated at high spatial resolution. However, available computing time is very limited. Since different areas of the ground surface require different minimum resolution, we propose a dynamic resolution terrain estimation.Based on support points, accumulated measurements are spatially smoothed to a continuous terrain model using maximum a posteriori estimation. Splitting the terrain into tiles, we dynamically adjust the support point resolution of single tiles, depending on their accuracy in areas of interest. Areas of interest are determined by fusing information on probable road areas from LiDAR and vision preprocessing steps.As demonstrated in real-world examples, our approach can model the terrain almost as accurately as if all tiles had the highest resolution, but with much less computational effort.
{"title":"Dynamic Resolution Terrain Estimation for Autonomous (Dirt) Road Driving Fusing LiDAR and Vision","authors":"Bianca Forkel, Hans-Joachim Wünsche","doi":"10.1109/iv51971.2022.9827214","DOIUrl":"https://doi.org/10.1109/iv51971.2022.9827214","url":null,"abstract":"For autonomous driving on rural or dirt roads-neither urban nor off-road - a large terrain area needs to be estimated at high spatial resolution. However, available computing time is very limited. Since different areas of the ground surface require different minimum resolution, we propose a dynamic resolution terrain estimation.Based on support points, accumulated measurements are spatially smoothed to a continuous terrain model using maximum a posteriori estimation. Splitting the terrain into tiles, we dynamically adjust the support point resolution of single tiles, depending on their accuracy in areas of interest. Areas of interest are determined by fusing information on probable road areas from LiDAR and vision preprocessing steps.As demonstrated in real-world examples, our approach can model the terrain almost as accurately as if all tiles had the highest resolution, but with much less computational effort.","PeriodicalId":184622,"journal":{"name":"2022 IEEE Intelligent Vehicles Symposium (IV)","volume":"15 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":"129286578","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/iv51971.2022.9827242
Abdulah Jarouf, N. Meskin, S. Al-Kuwari, Mohammad Shakerpour, C. Cassandras
Securing traffic flows in internet of vehicles (IoV) environments for connected and automated vehicles (CAVs) is a critical task as it should be done in real-time to allow vehicles’ controllers engagement on time. In this paper, the security of CAV communication at merging points is studied, the insecure vehicle communication is analysed in terms of the possible security threats and consequences, and security goals are then identified to protect the environment. We present a network topology that improves the availability of the system and propose a high-level design of a vehicle authentication protocol based on public key cryptography to authenticate vehicles. Simulation and analysis of the cryptographic functions are done to choose the best fit for vehicle communication, where Rivest-Shamir-Adleman (RSA)-2048 algorithms provide faster and more efficient computations.
{"title":"Security Analysis of Merging Control for Connected and Automated Vehicles","authors":"Abdulah Jarouf, N. Meskin, S. Al-Kuwari, Mohammad Shakerpour, C. Cassandras","doi":"10.1109/iv51971.2022.9827242","DOIUrl":"https://doi.org/10.1109/iv51971.2022.9827242","url":null,"abstract":"Securing traffic flows in internet of vehicles (IoV) environments for connected and automated vehicles (CAVs) is a critical task as it should be done in real-time to allow vehicles’ controllers engagement on time. In this paper, the security of CAV communication at merging points is studied, the insecure vehicle communication is analysed in terms of the possible security threats and consequences, and security goals are then identified to protect the environment. We present a network topology that improves the availability of the system and propose a high-level design of a vehicle authentication protocol based on public key cryptography to authenticate vehicles. Simulation and analysis of the cryptographic functions are done to choose the best fit for vehicle communication, where Rivest-Shamir-Adleman (RSA)-2048 algorithms provide faster and more efficient computations.","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":"126911026","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/iv51971.2022.9827143
Yasin Bayzidi, Alen Smajic, Fabian Hüger, Ruby L. V. Moritz, Serin Varghese, Peter Schlicht, Alois Knoll
Recent adversarial attacks with real world applications are capable of deceiving deep neural networks (DNN), which often appear as printed stickers applied to objects in physical world. Though achieving high success rate in lab tests and limited field tests, such attacks have not been tested on multiple DNN architectures with a standard setup to unveil the common robustness and weakness points of both the DNNs and the attacks. Furthermore, realistic looking stickers applied by normal people as acts of vandalism are not studied to discover their potential risks as well the risk of optimizing the location of such realistic stickers to achieve the maximum performance drop. In this paper, (a) we study the case of realistic looking sticker application effects on traffic sign detectors performance; (b) we use traffic sign image classification as our use case and train and attack 11 of the modern architectures for our analysis; (c) by considering different factors like brightness, blurriness and contrast of the train images in our sticker application procedure, we show that simple image processing techniques can help realistic looking stickers fit into their background to mimic real world tests; (d) by performing structured synthetic and real-world evaluations, we study the difference of various traffic sign classes in terms of their crucial distinctive features among the tested DNNs.
{"title":"Traffic Sign Classifiers Under Physical World Realistic Sticker Occlusions: A Cross Analysis Study","authors":"Yasin Bayzidi, Alen Smajic, Fabian Hüger, Ruby L. V. Moritz, Serin Varghese, Peter Schlicht, Alois Knoll","doi":"10.1109/iv51971.2022.9827143","DOIUrl":"https://doi.org/10.1109/iv51971.2022.9827143","url":null,"abstract":"Recent adversarial attacks with real world applications are capable of deceiving deep neural networks (DNN), which often appear as printed stickers applied to objects in physical world. Though achieving high success rate in lab tests and limited field tests, such attacks have not been tested on multiple DNN architectures with a standard setup to unveil the common robustness and weakness points of both the DNNs and the attacks. Furthermore, realistic looking stickers applied by normal people as acts of vandalism are not studied to discover their potential risks as well the risk of optimizing the location of such realistic stickers to achieve the maximum performance drop. In this paper, (a) we study the case of realistic looking sticker application effects on traffic sign detectors performance; (b) we use traffic sign image classification as our use case and train and attack 11 of the modern architectures for our analysis; (c) by considering different factors like brightness, blurriness and contrast of the train images in our sticker application procedure, we show that simple image processing techniques can help realistic looking stickers fit into their background to mimic real world tests; (d) by performing structured synthetic and real-world evaluations, we study the difference of various traffic sign classes in terms of their crucial distinctive features among the tested DNNs.","PeriodicalId":184622,"journal":{"name":"2022 IEEE Intelligent Vehicles Symposium (IV)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123177003","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/iv51971.2022.9827169
Wei Jiang, Xingyu Xing, An Huang, Junyi Chen
Visual-based perception systems are widely used in autonomous vehicles (AVs). In severe weather conditions, hazardous events of AVs may be induced by the performance limitations of perception system. We propose a staged analyzing method to quantitatively evaluate the performance limitations of visual-based perception system under severe weather conditions and explore the influence mechanism. In our method, the working process of visual-based perception systems is divided into two stages of image obtaining by camera and target recognition by recognition algorithm. Firstly, in image obtaining stage, the quality of images obtained in scenarios with different weather types and intensity is evaluated using monofactor analysis method. The relationship between different weather and metrics of image quality is analyzed. Secondly, in target recognition stage, metrics values of image quality and recognition results are fitted with (weighted) multiple linear regression model, and a regression model representing the influence relationship is acquired. Finally, the importance of indicators in image quality metrics is verified with BP neural network, and the performance of the regression model is analyzed with the results acquired in two example scenarios. With the obtained monofactor analysis results and the regression model, the influence mechanisms of high luminance and fog conditions are analyzed and compared, which shows the effectiveness of the method in performance limitation and its influence mechanism analysis.
{"title":"Research on Performance Limitations of Visual-based Perception System for Autonomous Vehicle under Severe Weather Conditions*","authors":"Wei Jiang, Xingyu Xing, An Huang, Junyi Chen","doi":"10.1109/iv51971.2022.9827169","DOIUrl":"https://doi.org/10.1109/iv51971.2022.9827169","url":null,"abstract":"Visual-based perception systems are widely used in autonomous vehicles (AVs). In severe weather conditions, hazardous events of AVs may be induced by the performance limitations of perception system. We propose a staged analyzing method to quantitatively evaluate the performance limitations of visual-based perception system under severe weather conditions and explore the influence mechanism. In our method, the working process of visual-based perception systems is divided into two stages of image obtaining by camera and target recognition by recognition algorithm. Firstly, in image obtaining stage, the quality of images obtained in scenarios with different weather types and intensity is evaluated using monofactor analysis method. The relationship between different weather and metrics of image quality is analyzed. Secondly, in target recognition stage, metrics values of image quality and recognition results are fitted with (weighted) multiple linear regression model, and a regression model representing the influence relationship is acquired. Finally, the importance of indicators in image quality metrics is verified with BP neural network, and the performance of the regression model is analyzed with the results acquired in two example scenarios. With the obtained monofactor analysis results and the regression model, the influence mechanisms of high luminance and fog conditions are analyzed and compared, which shows the effectiveness of the method in performance limitation and its influence mechanism analysis.","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":"126517289","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/iv51971.2022.9827248
Daniel Fusaro, Emilio Olivastri, D. Evangelista, Pietro Iob, A. Pretto
Self-driving vehicles and autonomous ground robots require a reliable and accurate method to analyze the traversability of the surrounding environment for safe navigation. This paper proposes a hybrid approach that combines geometric and appearance features for training Deep Encoder-Decoder architectures to detect the traversability score in real urban contexts. The proposed approach has been tested with two Deep Learning architectures on a public dataset of outdoor driving scenarios. Thanks to our approach, we are able to reach high levels of accuracy in detecting the correct traversability score in environments of highly variable complexity. This demonstrates the effectiveness and robustness of the proposed method.
{"title":"An Hybrid Approach to Improve the Performance of Encoder-Decoder Architectures for Traversability Analysis in Urban Environments","authors":"Daniel Fusaro, Emilio Olivastri, D. Evangelista, Pietro Iob, A. Pretto","doi":"10.1109/iv51971.2022.9827248","DOIUrl":"https://doi.org/10.1109/iv51971.2022.9827248","url":null,"abstract":"Self-driving vehicles and autonomous ground robots require a reliable and accurate method to analyze the traversability of the surrounding environment for safe navigation. This paper proposes a hybrid approach that combines geometric and appearance features for training Deep Encoder-Decoder architectures to detect the traversability score in real urban contexts. The proposed approach has been tested with two Deep Learning architectures on a public dataset of outdoor driving scenarios. Thanks to our approach, we are able to reach high levels of accuracy in detecting the correct traversability score in environments of highly variable complexity. This demonstrates the effectiveness and robustness of the proposed method.","PeriodicalId":184622,"journal":{"name":"2022 IEEE Intelligent Vehicles Symposium (IV)","volume":"25 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":"126064293","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/iv51971.2022.9827365
Hong Wang, Liang Peng, Jun Li, Wen-Hui Yu, Xiong Xiong
The safety of autonomous vehicles is hard to ensure in adverse weather since the sensors will degrade drastically. Setting a variable speed limit based on real-time weather condition is the most efficient method to make the vehicle safe. But most current speed limit methods are based on human visibility rather than the sensor, which is not suitable for autonomous vehicles. Thus, it is necessary to explore the performance of sensors in different weathers and propose a speed limit method based on sensor performance. Safety decisions will be made based on the calculated speed limit to ensure safety.This paper describes how to make safety decisions based on sensor performance and road conditions in real-time. The experiment explores the degradation of different sensors, and variable speed limit methods are proposed for rainy and foggy days. MPC controller is used to generate safety decisions.
{"title":"Safety Decision of Running Speed Based on Real-time Weather","authors":"Hong Wang, Liang Peng, Jun Li, Wen-Hui Yu, Xiong Xiong","doi":"10.1109/iv51971.2022.9827365","DOIUrl":"https://doi.org/10.1109/iv51971.2022.9827365","url":null,"abstract":"The safety of autonomous vehicles is hard to ensure in adverse weather since the sensors will degrade drastically. Setting a variable speed limit based on real-time weather condition is the most efficient method to make the vehicle safe. But most current speed limit methods are based on human visibility rather than the sensor, which is not suitable for autonomous vehicles. Thus, it is necessary to explore the performance of sensors in different weathers and propose a speed limit method based on sensor performance. Safety decisions will be made based on the calculated speed limit to ensure safety.This paper describes how to make safety decisions based on sensor performance and road conditions in real-time. The experiment explores the degradation of different sensors, and variable speed limit methods are proposed for rainy and foggy days. MPC controller is used to generate safety decisions.","PeriodicalId":184622,"journal":{"name":"2022 IEEE Intelligent Vehicles Symposium (IV)","volume":"184 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":"121191792","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}
Intelligent intersection management aims to schedule vehicles so that vehicles can pass through an intersection efficiently and safely. However, inaccurate control, imperfect communication, and malicious information or behavior lead to robustness issues of intelligent intersection management. In this work, we focus on improving robustness against deadlocks by changing the trajectories of vehicles. To guarantee the resolvability of deadlocks, we limit the number of vehicles in an intersection to be smaller than or equal to an intersection-specific value called the maximal deadlock-free load. We develop an algorithm to compute the maximal deadlock-free load. We further reduce the computation time by computing the loads which are pessimistic (smaller) but still deadlock-free. Since the maximal deadlock-free load only depends on the given intersection, it can be integrated with different scheduling algorithms. Experimental results demonstrate that, by changing the trajectories of vehicles and limiting the number of vehicles under maximal deadlock-free loads, our approach can guarantee deadlock-freeness and maintain good traffic efficiency.
{"title":"Deadlock Resolution for Intelligent Intersection Management with Changeable Trajectories","authors":"Li-Heng Lin, Kuan-Chun Wang, Ying-Hua Lee, Kai-En Lin, Chung-Wei Lin, I. Jiang","doi":"10.1109/iv51971.2022.9827323","DOIUrl":"https://doi.org/10.1109/iv51971.2022.9827323","url":null,"abstract":"Intelligent intersection management aims to schedule vehicles so that vehicles can pass through an intersection efficiently and safely. However, inaccurate control, imperfect communication, and malicious information or behavior lead to robustness issues of intelligent intersection management. In this work, we focus on improving robustness against deadlocks by changing the trajectories of vehicles. To guarantee the resolvability of deadlocks, we limit the number of vehicles in an intersection to be smaller than or equal to an intersection-specific value called the maximal deadlock-free load. We develop an algorithm to compute the maximal deadlock-free load. We further reduce the computation time by computing the loads which are pessimistic (smaller) but still deadlock-free. Since the maximal deadlock-free load only depends on the given intersection, it can be integrated with different scheduling algorithms. Experimental results demonstrate that, by changing the trajectories of vehicles and limiting the number of vehicles under maximal deadlock-free loads, our approach can guarantee deadlock-freeness and maintain good traffic efficiency.","PeriodicalId":184622,"journal":{"name":"2022 IEEE Intelligent Vehicles Symposium (IV)","volume":"101 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":"121368510","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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