SAE International Journal of Connected and Automated Vehicles最新文献

{"title":"Effect of Platoon Configurations on the Anti-Disturbing\u0000 Performance","authors":"Xiangji Wu, Xiujian Yang, Shengbin Zhang, Shenyi Wang","doi":"10.4271/12-07-01-0003","DOIUrl":"https://doi.org/10.4271/12-07-01-0003","url":null,"abstract":"In order to promote the actual application of the vehicular platoon, this study\u0000 investigates the effect of the specific platoon configurations including\u0000 predecessor following (PF), predecessor–leader following (PLF), and\u0000 bidirectional following (BD), on the anti-disturbing performance from the linear\u0000 to nonlinear perspective. First, based on the method of sensitivity of error\u0000 propagation to the disturbance, a linear platoon model is established by\u0000 considering an individual vehicle as a lumped-mass point. Then, the transfer\u0000 function matrix from disturbance to spacing error is derived for sensitivity\u0000 analysis. Finally, especially considering the inherent vehicle dynamics, the\u0000 Burckhardt tire force model is adopted to construct a nonlinear platoon dynamics\u0000 model for the nonlinear dynamics analysis. The results reveal the\u0000 characteristics of each platoon configuration, as well as the design of control\u0000 gains in terms of the anti-disturbing performance. The nonlinear dynamics\u0000 property in high-adhesion conditions are generally similar to those of the\u0000 sensitivity analysis based on the linear platoon model. However, some particular\u0000 and complex phenomena different from the linear sensitivity analysis especially\u0000 in low-adhesion conditions are only observed by the nonlinear dynamics\u0000 analysis.","PeriodicalId":417451,"journal":{"name":"SAE International Journal of Connected and Automated Vehicles","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121325926","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}

{"title":"A Quantitative Analysis of Autonomous Vehicle Cybersecurity as a\u0000 Component of Trust","authors":"Warren King, L. Halawi","doi":"10.4271/12-07-01-0002","DOIUrl":"https://doi.org/10.4271/12-07-01-0002","url":null,"abstract":"Connected autonomous vehicles that employ internet connectivity are\u0000 technologically complex, which makes them vulnerable to cyberattacks. Many\u0000 cybersecurity researchers, white hat hackers, and black hat hackers have\u0000 discovered numerous exploitable vulnerabilities in connected vehicles. Several\u0000 studies indicate consumers do not fully trust automated driving systems. This\u0000 study expanded the technology acceptance model (TAM) to include cybersecurity\u0000 and level of trust as determinants of technology acceptance. This study surveyed\u0000 a diverse sample of 209 licensed US drivers over 18 years old. Results indicated\u0000 that perceived ease of use positively influences perceived usefulness, perceived\u0000 ease of usefulness negatively influences perceived cyber threats, and perceived\u0000 cyber threats negatively influence the level of trust.","PeriodicalId":417451,"journal":{"name":"SAE International Journal of Connected and Automated Vehicles","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125130864","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}

{"title":"Prediction and Control of Connected Mixed Traffic under Different\u0000 Information Flow Topologies","authors":"Longxiang Guo, Yunyi Jia","doi":"10.4271/12-07-01-0001","DOIUrl":"https://doi.org/10.4271/12-07-01-0001","url":null,"abstract":"Different platoon controls of connected automated vehicles have been studied to\u0000 improve the entire fleet’s overall energy efficiency and driving safety. The\u0000 platoons can be used during highway cruising to reduce unnecessary braking,\u0000 shorten required headway, and thus improve traffic capacity and fuel economy.\u0000 They can also be used in urban driving to improve traffic efficiency at\u0000 intersections. However, there remain two problems that prevent the technology\u0000 from achieving maximum benefit. First, the presence of human-driven vehicles\u0000 will change the behavior of the fleet and platoon control of connected mixed\u0000 traffic. Second, the communication uncertainties impose negative impacts on the\u0000 dynamics of the platoon. A high-performance state predictor for surrounding\u0000 vehicles can reduce the human-driven vehicle’s influence and help handle\u0000 communication uncertainties better. This article proposes a novel inverse model\u0000 predictive control (IMPC)-based approach to capture and predict longitudinal\u0000 human driving behaviors. It is also leveraged to formulate an efficient ego\u0000 vehicle model predictive control (MPC) approach to handle random communication\u0000 delays and packet losses in three different communication topologies: the\u0000 predecessor following, the predecessor–leader following, and the\u0000 multi-predecessor following. The proposed approach is compared with several\u0000 prediction approaches in simulation to demonstrate its effectiveness and find\u0000 the appropriate communication topology for mixed traffic platoon control. The\u0000 results show that the predecessor–leader following topology can enhance the\u0000 benefits of the integrated model predictive control strategy. Specifically, it\u0000 can lower the average control errors of the following connected and automated\u0000 vehicles by more than 50% and decrease the control efforts by 10%.","PeriodicalId":417451,"journal":{"name":"SAE International Journal of Connected and Automated Vehicles","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125702973","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}

{"title":"Sliced Secure Boot an Accelerated Secure Boot Concept Compatible with\u0000 Automotive Safety Controllers","authors":"R. Kaster, Di Ma","doi":"10.4271/12-06-03-0024","DOIUrl":"https://doi.org/10.4271/12-06-03-0024","url":null,"abstract":"The secure boot has successfully protected systems from executing untrusted\u0000 software (SW), but low-power controllers lack sufficient time to check every\u0000 memory cell while satisfying real-time functional safety requirements.\u0000 Automotive controllers need to maintain security through multiple cycles of\u0000 remote, unsupervised operation and safely reach a secure state when an anomaly\u0000 is detected. To accelerate the boot time, we propose Sliced Secure Boot: build\u0000 fingerprints by slicing orthogonally through memory blocks, protect each cell\u0000 with a reusable fingerprint using a reproducible pattern with sufficient\u0000 entropy, and randomly check one fingerprint pattern during boot. We do not claim\u0000 that sampling offers equivalent protection to exhaustive checks but demonstrate\u0000 that careful sampling can provide a sufficient level of detection while\u0000 maintaining compatibility with both startup time and functional safety\u0000 requirements.","PeriodicalId":417451,"journal":{"name":"SAE International Journal of Connected and Automated Vehicles","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130834667","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}

{"title":"Recognition Assistance Interface for Human-Automation Cooperation in\u0000 Pedestrian Risk Prediction","authors":"Atsushi Kuribayashi, E. Takeuchi, Alexander Carballo, Yoshio Ishiguro, K. Takeda","doi":"10.4271/12-06-03-0023","DOIUrl":"https://doi.org/10.4271/12-06-03-0023","url":null,"abstract":"Autonomous driving systems (ADS) have been widely tested in real-world\u0000 environments with operators who must monitor and intervene due to remaining\u0000 technical challenges. However, intervention methods that require operators to\u0000 take over control of the vehicle involve many drawbacks related to human\u0000 performance. ADS consist of recognition, decision, and control modules. The\u0000 latter two phases are dependent on the recognition phase, which still struggles\u0000 with tasks involving the prediction of human behavior, such as pedestrian risk\u0000 prediction. As an alternative to full automation of the recognition task,\u0000 cooperative recognition approaches utilize the human operator to assist the\u0000 automated system in performing challenging recognition tasks, using a\u0000 recognition assistance interface to realize human-machine cooperation. In this\u0000 study, we propose a recognition assistance interface for cooperative recognition\u0000 in order to achieve safer and more efficient driving through improved\u0000 human-automation cooperation. A simulator experiment with 18 participants is\u0000 conducted to evaluate our recognition assistance interface in comparison with a\u0000 conventional control intervention, in terms of driving safety, efficiency, and\u0000 usability. Recognition of pedestrian crossing intention is selected for the\u0000 cooperation task, and driving scenarios in which the automated system cannot\u0000 reliably recognize the crossing intentions of pedestrians at non-signalized\u0000 locations are selected as the driving scenario. Statistical analysis of our\u0000 experimental results reveals that the proposed recognition assistance interface\u0000 allowed more accurate operator intervention, was easier to use, and achieved\u0000 more stable vehicle control than the control intervention. We also found that\u0000 sharing the recognition information of the automated driving system with\u0000 operators could divide their attention, impairing intervention performance. Our\u0000 experimental results suggest that the unifying presentation of the system\u0000 recognition information and the operator’s manipulation target on the\u0000 touchscreen of the user interface addresses this problem.","PeriodicalId":417451,"journal":{"name":"SAE International Journal of Connected and Automated Vehicles","volume":"69 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129410887","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}

{"title":"Localization in Global Positioning System–Denied Environments Using\u0000 Infrastructure-Embedded Analog-Digital Information","authors":"Samin Moosavi, Andrew Weaver, Swaminathan Gopalswamy","doi":"10.4271/12-06-04-0029","DOIUrl":"https://doi.org/10.4271/12-06-04-0029","url":null,"abstract":"While a majority of transportation and mobility solutions rely on in-vehicle\u0000 sensors and the availability of the global positioning system (GPS) for absolute\u0000 localization, alternate paradigms leveraging smart infrastructure have started\u0000 becoming a viable solution for localization without needing GPS. However, the\u0000 majority of approaches involving smart infrastructure require a means for\u0000 wireless communication. In this article, we describe a novel method that can\u0000 accurately localize the vehicle without using GPS and wireless communication by\u0000 leveraging embedded digital and analog information on the roadside signage. The\u0000 embedded information consists of a digital signature which can be used to\u0000 cross-reference the ground truth (GT) location of the signage, as well as\u0000 geometric information of the signage. This information is directly leveraged by\u0000 on-vehicle sensors to generate absolute localization information. Specifically,\u0000 the smart infrastructure consists of signage that is visible primarily in the\u0000 infrared (IR) spectrum. A specialized camera that is optimized to read the\u0000 digital signature extracts the analog information associated with the signage\u0000 (ground truth and geometry). This is then used by both the camera, as well as a\u0000 millimeter (mm)-wave radar to produce independent localization information. The\u0000 camera and radar information are correlated with the signage information using a\u0000 global nearest neighbor algorithm, followed by fusion with vehicle odometry\u0000 using an extended Kalman filter (EKF) to generate accurate localization of the\u0000 vehicle. The EKF is set up to manage asynchronous observations between the\u0000 camera, radar, and vehicle odometry. The proposed method is implemented to\u0000 localize a vehicle without the aid of GPS, and the results show consistent\u0000 localization with the root mean squared (RMS) longitudinal and lateral errors\u0000 less than 0.46 m and 0.19 m, respectively.","PeriodicalId":417451,"journal":{"name":"SAE International Journal of Connected and Automated Vehicles","volume":"61 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133711301","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}

{"title":"Classification of Roadway Infrastructure and Collaborative Automated\u0000 Driving System","authors":"B. Ran, Yang Cheng, Shen Li, Hanchu Li, Steven Parker","doi":"10.4271/12-06-04-0026","DOIUrl":"https://doi.org/10.4271/12-06-04-0026","url":null,"abstract":"The latest developments in vehicle-to-infrastructure (V2I) and\u0000 vehicle-to-anything (V2X) technologies enable all the entities in the\u0000 transportation system to communicate and collaborate to optimize transportation\u0000 safety, mobility, and equity at the system level. On the other hand, the\u0000 community of researchers and developers is becoming aware of the critical role\u0000 of roadway infrastructure in realizing automated driving. In particular,\u0000 intelligent infrastructure systems, which leverage modern sensors, artificial\u0000 intelligence, and communication capabilities, can provide critical information\u0000 and control support to connected and/or automated vehicles to fulfill functions\u0000 that are infeasible for automated vehicles alone due to technical or cost\u0000 considerations. However, there is limited research on formulating and\u0000 standardizing the intelligence levels of road infrastructure to facilitate the\u0000 development, as the SAE automated driving levels have done for automated\u0000 vehicles. This article proposes a five-level intelligence definition for\u0000 intelligent roadway infrastructure, namely, connected and automated highway\u0000 (CAH). The CAH is a subsystem of the more extensive collaborative automated\u0000 driving system (CADS), along with the connected automated vehicle (CAV)\u0000 subsystem. Leveraging the intelligence definition of CAH, the intelligence\u0000 definition for the CADS is also defined. Examples of how the CAH at different\u0000 levels operates with the CAV in the CADS are also introduced to demonstrate the\u0000 dynamic allocation of various automated driving tasks between different entities\u0000 in the CADS.","PeriodicalId":417451,"journal":{"name":"SAE International Journal of Connected and Automated Vehicles","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124117546","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}

{"title":"A Multiagency Long Short-Term Model Beamforming Prediction Model for\u0000 Cellular Vehicle to Everything","authors":"Vivekanandh Elangovan, Weidong Xiang, Sheng Liu","doi":"10.4271/12-06-04-0030","DOIUrl":"https://doi.org/10.4271/12-06-04-0030","url":null,"abstract":"Machine learning (ML) for predicting wireless channels of vehicular\u0000 communications networks has attracted interest in recent years. Beamforming is a\u0000 technique used to selectively transmit and receive data in a desired direction.\u0000 The receiver should be capable of choosing the right beam at the right time. The\u0000 usage of adaptive antenna scanning, i.e., scanning all the beams and choosing\u0000 the best beam will result only in 30% accuracy, which means 70% of the data will\u0000 be lost. This article studied a multiagency long short-term memory (LSTM)\u0000 beamforming prediction model based on signal strength to forecast optimum beams\u0000 within each beacon interval (BI) for cellular vehicle-to-everything (C-V2X)\u0000 systems. The model combines the outputs of several parallel prediction models\u0000 resulting in an enhanced accuracy of prediction. Simulation data validated the\u0000 effectiveness of the proposed prediction model on the university campus,\u0000 resulting in a 24% improvement in prediction accuracy.","PeriodicalId":417451,"journal":{"name":"SAE International Journal of Connected and Automated Vehicles","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133393361","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}

{"title":"Using Radio Technical Commission for Maritime Services Corrections in\u0000 a Consumer-Grade Lane-Level Positioning System for Connected\u0000 Vehicles","authors":"Nigel Williams, Alexander Vu, Guoyuan Wu, M. Barth, Kun Zhou","doi":"10.4271/12-06-04-0028","DOIUrl":"https://doi.org/10.4271/12-06-04-0028","url":null,"abstract":"Connected vehicle (CV) technology has the potential to greatly improve the\u0000 safety, mobility, and environmental sustainability of traffic. Many CV\u0000 applications require the vehicle position as input, which is primarily provided\u0000 by global navigation satellite systems (GNSS). Although a large number of those\u0000 applications (e.g., Intersection Movement Assist) require vehicle positioning to\u0000 have lane-level accuracy, it has been shown that the type of positioning system\u0000 typically used by CVs currently cannot provide consistent lane-level accuracy,\u0000 even under open-sky conditions. In order to address this gap, we have evaluated\u0000 an enhanced positioning system that adds little, if any, to the cost of the CV.\u0000 It consists of a single-frequency real-time kinematic (RTK)-capable GNSS\u0000 receiver onboard the vehicle, which utilizes Radio Technical Commission for\u0000 Maritime Services (RTCM) differential corrections transmitted over dedicated\u0000 short-range communications (DSRC) by the roadside infrastructure. Tests on a\u0000 moving vehicle show that this system could provide lane-level accuracy over 95%\u0000 of the time in open-sky conditions. These tests also show the DSRC to be an\u0000 effective means of disseminating RTCM corrections, given the intersection\u0000 spacings and communication ranges in the test. However, neither RTCM nor the\u0000 more commonly used space-based augmentation system (SBAS) differential\u0000 corrections appeared to improve the positioning accuracy of GNSS in urban\u0000 canyons.","PeriodicalId":417451,"journal":{"name":"SAE International Journal of Connected and Automated Vehicles","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125553208","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}

{"title":"Implementation of the Correction Algorithm in an Environment with\u0000 Dynamic Actors","authors":"Mark Omwansa, Rick Meyer","doi":"10.4271/12-06-03-0021","DOIUrl":"https://doi.org/10.4271/12-06-03-0021","url":null,"abstract":"Safe navigation of an autonomous vehicle (AV) requires a fast and correct\u0000 perception of its driving environment. Meaning, the AV needs to persistently\u0000 detect and track moving objects around it with high accuracy for safe\u0000 navigation. These tasks of detection and tracking are performed by the AV\u0000 perception system that utilizes data from sensors such as LIDARs, radars, and\u0000 cameras. The majority of AVs are typically fitted with multiple sensors to\u0000 create redundancy and avoid dependence on a single sensor. This strategy has\u0000 been shown to yield accurate perception results when the sensors work well and\u0000 are calibrated correctly. However, over time, the cumulative use of the AV or\u0000 poor placement of sensors may lead to faults that need correcting. This article\u0000 proposes an online algorithm that corrects the faulty perception of an AV by\u0000 determining a set of transformations that would align a cluster of measurements,\u0000 from a moving vehicle in the scene to a corresponding detection in an image\u0000 taken by the synchronized, forward-facing camera of the AV. The correction\u0000 algorithm is first tested, assuming the availability of ground truth information\u0000 to correct the LIDAR, and then tested with camera images which are used to\u0000 determine ground truth. The comparison metric between expected and optimal\u0000 parameters is the mean absolute error (MAE). The translation, scale, and\u0000 orientation errors between the expected and optimal parameters when using ground\u0000 truth data in the correction algorithm are 9.41 × 10–4 m, 3.84 ×\u0000 10–7, and 3.82 × 10–2 degrees, respectively; and the\u0000 errors for camera images are 0.414 m, 0.017, and 0.007 degrees,\u0000 respectively.","PeriodicalId":417451,"journal":{"name":"SAE International Journal of Connected and Automated Vehicles","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126828772","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}