Pub Date : 2015-08-27DOI: 10.1109/IVS.2015.7225895
Youngil Koh, Hyundong Her, Kilsoo Kim, K. Yi
This paper describes an integrated driver model for vehicle-driver closed-loop simulation at high speed maneuvering. The proposed driver model is developed to specialize in limit handling, in order to be used as a validation platform of chassis control system. Thus, the proposed driver model emulates human driver's driving characteristics such as, desired path selection from varying preview area, deceleration against losing maneuverability. In high-speed cornering, steering with excessive corner-entry speed causes lateral tire force saturation readily. Sequentially, the lateral tire force saturation induces lateral instability of a vehicle. Deceleration is the most effective manipulation which driver can do. The proposed driver model is designed to utilize capability of tire force tightly, while securing lateral stability of the vehicle. The proposed driver model has been validated via comparison with an expert driver's driving data, collected on the Korea International Circuit in Yeongam, Korea.
{"title":"Driver model with motion stabilizer for vehicle-driver closed-loop simulation at high-speed maneuvering","authors":"Youngil Koh, Hyundong Her, Kilsoo Kim, K. Yi","doi":"10.1109/IVS.2015.7225895","DOIUrl":"https://doi.org/10.1109/IVS.2015.7225895","url":null,"abstract":"This paper describes an integrated driver model for vehicle-driver closed-loop simulation at high speed maneuvering. The proposed driver model is developed to specialize in limit handling, in order to be used as a validation platform of chassis control system. Thus, the proposed driver model emulates human driver's driving characteristics such as, desired path selection from varying preview area, deceleration against losing maneuverability. In high-speed cornering, steering with excessive corner-entry speed causes lateral tire force saturation readily. Sequentially, the lateral tire force saturation induces lateral instability of a vehicle. Deceleration is the most effective manipulation which driver can do. The proposed driver model is designed to utilize capability of tire force tightly, while securing lateral stability of the vehicle. The proposed driver model has been validated via comparison with an expert driver's driving data, collected on the Korea International Circuit in Yeongam, Korea.","PeriodicalId":294701,"journal":{"name":"2015 IEEE Intelligent Vehicles Symposium (IV)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132357104","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 : 2015-08-27DOI: 10.1109/IVS.2015.7225710
J. Wiest, Matthias Karg, Felix Kunz, Stephan Reuter, U. Kressel, K. Dietmayer
This contribution proposes a novel algorithm for predicting maneuvers at intersections. With applicability to driver assistance systems and autonomous driving, the presented methodology estimates a maneuver probability for every possible direction at an intersection. For this purpose, a generic intersection-feature, space-based representation is defined which combines static and dynamic intersection information with the dynamic properties of the observed vehicle, provided by a tracking module. A statistical behavior model is learned from previously recorded patterns by approximating the resulting feature space. Because the feature space consists of different types of features (mixed-feature space), a Bernoulli-Gaussian Mixture Model is applied as approximating function. Further, an online learning extension is proposed to adapt the model to the characteristics of different intersections.
{"title":"A probabilistic maneuver prediction framework for self-learning vehicles with application to intersections","authors":"J. Wiest, Matthias Karg, Felix Kunz, Stephan Reuter, U. Kressel, K. Dietmayer","doi":"10.1109/IVS.2015.7225710","DOIUrl":"https://doi.org/10.1109/IVS.2015.7225710","url":null,"abstract":"This contribution proposes a novel algorithm for predicting maneuvers at intersections. With applicability to driver assistance systems and autonomous driving, the presented methodology estimates a maneuver probability for every possible direction at an intersection. For this purpose, a generic intersection-feature, space-based representation is defined which combines static and dynamic intersection information with the dynamic properties of the observed vehicle, provided by a tracking module. A statistical behavior model is learned from previously recorded patterns by approximating the resulting feature space. Because the feature space consists of different types of features (mixed-feature space), a Bernoulli-Gaussian Mixture Model is applied as approximating function. Further, an online learning extension is proposed to adapt the model to the characteristics of different intersections.","PeriodicalId":294701,"journal":{"name":"2015 IEEE Intelligent Vehicles Symposium (IV)","volume":"106 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133974792","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 : 2015-08-27DOI: 10.1109/IVS.2015.7225896
Teng Teng, Luzheng Bi, Xinan Fan
This paper proposes a novel method to recognize an emergency situation by translating EEG signals of a disabled driver while he or she uses a brain-machine interface without using his or her limbs to drive a vehicle. EEG signals were first filtered by independent component analysis along with information entropy. And then the sums of powers of theta wave in the power spectrum of EEG signals from 13 channels were used as features of the classifier built by linear discriminant analysis. The pilot experimental results from two participants in a driving simulator indicated that the model recognized emergency situations (e.g., pedestrian sudden occurrence) 400 ms earlier than the response of drivers with a hit rate of 76.4%, suggesting that the proposed method is feasible. The proposed method can be used as a complementary method to the existing ones based on detecting external objects with sensors.
{"title":"Using EEG to recognize emergency situations for brain-controlled vehicles","authors":"Teng Teng, Luzheng Bi, Xinan Fan","doi":"10.1109/IVS.2015.7225896","DOIUrl":"https://doi.org/10.1109/IVS.2015.7225896","url":null,"abstract":"This paper proposes a novel method to recognize an emergency situation by translating EEG signals of a disabled driver while he or she uses a brain-machine interface without using his or her limbs to drive a vehicle. EEG signals were first filtered by independent component analysis along with information entropy. And then the sums of powers of theta wave in the power spectrum of EEG signals from 13 channels were used as features of the classifier built by linear discriminant analysis. The pilot experimental results from two participants in a driving simulator indicated that the model recognized emergency situations (e.g., pedestrian sudden occurrence) 400 ms earlier than the response of drivers with a hit rate of 76.4%, suggesting that the proposed method is feasible. The proposed method can be used as a complementary method to the existing ones based on detecting external objects with sensors.","PeriodicalId":294701,"journal":{"name":"2015 IEEE Intelligent Vehicles Symposium (IV)","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132528378","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 : 2015-08-27DOI: 10.1109/IVS.2015.7225744
Jaebum Choi, M. Maurer
Simultaneous localization and mapping (SLAM) plays a significant role in autonomous vehicles when a global navigation satellite system (GNSS) is not available. Environment models and underlying estimation techniques are key factors of this algorithm. In this paper, we present a hybrid map-based SLAM approach using Rao-Blackwellized particle filters (RBPFs). We represent the environment with the hybrid map which consists of feature and grid maps. The joint posterior between the vehicle positions and both maps are maintained using RBPFs. This approach allows a vehicle to update its states in a more robust and efficient way. We derived a novel sampling formula by combining a feature measurement likelihood to the traditional grid-based SLAM framework and can decrease the uncertainty of the predicted vehicle position significantly. Moreover, we represent the grid maps with 3D models because 2D models could be insufficient and less reliable to achieve tasks such as navigation and obstacle avoidance in complex 3D environment. We are also able to show that the 3D grid measurement likelihood has a lower variance and with that we can improve the overall performance of the algorithm.
{"title":"Simultaneous localization and mapping based on the local volumetric hybrid map","authors":"Jaebum Choi, M. Maurer","doi":"10.1109/IVS.2015.7225744","DOIUrl":"https://doi.org/10.1109/IVS.2015.7225744","url":null,"abstract":"Simultaneous localization and mapping (SLAM) plays a significant role in autonomous vehicles when a global navigation satellite system (GNSS) is not available. Environment models and underlying estimation techniques are key factors of this algorithm. In this paper, we present a hybrid map-based SLAM approach using Rao-Blackwellized particle filters (RBPFs). We represent the environment with the hybrid map which consists of feature and grid maps. The joint posterior between the vehicle positions and both maps are maintained using RBPFs. This approach allows a vehicle to update its states in a more robust and efficient way. We derived a novel sampling formula by combining a feature measurement likelihood to the traditional grid-based SLAM framework and can decrease the uncertainty of the predicted vehicle position significantly. Moreover, we represent the grid maps with 3D models because 2D models could be insufficient and less reliable to achieve tasks such as navigation and obstacle avoidance in complex 3D environment. We are also able to show that the 3D grid measurement likelihood has a lower variance and with that we can improve the overall performance of the algorithm.","PeriodicalId":294701,"journal":{"name":"2015 IEEE Intelligent Vehicles Symposium (IV)","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133909439","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 : 2015-08-27DOI: 10.1109/IVS.2015.7225841
Brian K. Mok, Mishel Johns, Key Jung Lee, Hillary Page Ive, D. Miller, Wendy Ju
With automated driving systems, drivers may still be expected to resume full control of the vehicle. While structured transitions where drivers are given warning are desirable, it is critical to benchmark how drivers perform when transition of control is unstructured and occurs without advanced warning. In this study, we observed how participants (N=27) in a driving simulator performed after they were subjected to an emergency loss of automation. We tested three transition time conditions, with an unstructured transition of vehicle control occurring 2 seconds, 5 seconds, or 8 seconds before the participants encountered a road hazard that required the drivers' intervention. Few drivers in the 2 second condition were able to safely negotiate the road hazard situation, while the majority of drivers in 5 or 8 second conditions were able to navigate the hazard safely. Similarly, drivers in 2 second condition rated the vehicle to be less likeable than drivers in 5 and 8 second conditions. From the study results, we are able to narrow in on a minimum amount of time in which drivers can take over the control of vehicle safely and comfortably from the automated system in the advent of an impending road hazard.
{"title":"Timing of unstructured transitions of control in automated driving","authors":"Brian K. Mok, Mishel Johns, Key Jung Lee, Hillary Page Ive, D. Miller, Wendy Ju","doi":"10.1109/IVS.2015.7225841","DOIUrl":"https://doi.org/10.1109/IVS.2015.7225841","url":null,"abstract":"With automated driving systems, drivers may still be expected to resume full control of the vehicle. While structured transitions where drivers are given warning are desirable, it is critical to benchmark how drivers perform when transition of control is unstructured and occurs without advanced warning. In this study, we observed how participants (N=27) in a driving simulator performed after they were subjected to an emergency loss of automation. We tested three transition time conditions, with an unstructured transition of vehicle control occurring 2 seconds, 5 seconds, or 8 seconds before the participants encountered a road hazard that required the drivers' intervention. Few drivers in the 2 second condition were able to safely negotiate the road hazard situation, while the majority of drivers in 5 or 8 second conditions were able to navigate the hazard safely. Similarly, drivers in 2 second condition rated the vehicle to be less likeable than drivers in 5 and 8 second conditions. From the study results, we are able to narrow in on a minimum amount of time in which drivers can take over the control of vehicle safely and comfortably from the automated system in the advent of an impending road hazard.","PeriodicalId":294701,"journal":{"name":"2015 IEEE Intelligent Vehicles Symposium (IV)","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133483292","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 : 2015-08-27DOI: 10.1109/IVS.2015.7225737
Mario Passani, J. J. Torres, L. Bergasa
The future of autonomous vehicles and driver assistance systems is underpinned by the need of fast and efficient approaches for road scene understanding. Despite the large explored paths for road detection, there is still a research gap for incorporating image understanding capabilities in intelligent vehicles. This paper presents a pixelwise segmentation of roads from monocular images. The proposal is based on a probabilistic graphical model and a set of algorithms and configurations chosen to speed up the inference of the road pixels. In brief, the proposed method employs Conditional Random Fields and Uniformly Reweighted Belief Propagation. Besides, the approach is ranked on the KITTI ROAD dataset yielding state-of-the-art results with the lowest runtime per image using a standard PC.
{"title":"Fast pixelwise road inference based on Uniformly Reweighted Belief Propagation","authors":"Mario Passani, J. J. Torres, L. Bergasa","doi":"10.1109/IVS.2015.7225737","DOIUrl":"https://doi.org/10.1109/IVS.2015.7225737","url":null,"abstract":"The future of autonomous vehicles and driver assistance systems is underpinned by the need of fast and efficient approaches for road scene understanding. Despite the large explored paths for road detection, there is still a research gap for incorporating image understanding capabilities in intelligent vehicles. This paper presents a pixelwise segmentation of roads from monocular images. The proposal is based on a probabilistic graphical model and a set of algorithms and configurations chosen to speed up the inference of the road pixels. In brief, the proposed method employs Conditional Random Fields and Uniformly Reweighted Belief Propagation. Besides, the approach is ranked on the KITTI ROAD dataset yielding state-of-the-art results with the lowest runtime per image using a standard PC.","PeriodicalId":294701,"journal":{"name":"2015 IEEE Intelligent Vehicles Symposium (IV)","volume":"63 5","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120923615","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 : 2015-08-27DOI: 10.1109/IVS.2015.7225756
Andra Petrovai, R. Danescu, S. Nedevschi
This paper presents SmartCoDrive, an Android application which performs driving assistance functions: 3D lane detection and tracking, forward obstacle detection, obstacle tracking. With this mobile application we wish to increase the adoption rate of driving assistance systems and to provide a viable and cheap solution for every driver, that will be able to use his own tablet or smartphone as a personal driving assistant. The mobile application is deployed on a tablet equipped with dual back-facing cameras. The visual information from the two cameras, along with the data received from the Controller Area Network bus of the vehicle enable a thorough understanding of the 3D environment. First, we develop the sparse 3D reconstruction algorithm. Then, using monocular vision we perform lane markings detection. Obstacle detection is done by combining the superpixel segmentation with 3D information and the tracking algorithm is based on the Kalman Filter. Since the processing capabilities of the mobile platforms are limited, different optimizations are carried out in order to obtain a real-time implementation. The Android application may be used in urban traffic that is characterized by low-speed and short-medium distances to obstacles.
{"title":"A stereovision based approach for detecting and tracking lane and forward obstacles on mobile devices","authors":"Andra Petrovai, R. Danescu, S. Nedevschi","doi":"10.1109/IVS.2015.7225756","DOIUrl":"https://doi.org/10.1109/IVS.2015.7225756","url":null,"abstract":"This paper presents SmartCoDrive, an Android application which performs driving assistance functions: 3D lane detection and tracking, forward obstacle detection, obstacle tracking. With this mobile application we wish to increase the adoption rate of driving assistance systems and to provide a viable and cheap solution for every driver, that will be able to use his own tablet or smartphone as a personal driving assistant. The mobile application is deployed on a tablet equipped with dual back-facing cameras. The visual information from the two cameras, along with the data received from the Controller Area Network bus of the vehicle enable a thorough understanding of the 3D environment. First, we develop the sparse 3D reconstruction algorithm. Then, using monocular vision we perform lane markings detection. Obstacle detection is done by combining the superpixel segmentation with 3D information and the tracking algorithm is based on the Kalman Filter. Since the processing capabilities of the mobile platforms are limited, different optimizations are carried out in order to obtain a real-time implementation. The Android application may be used in urban traffic that is characterized by low-speed and short-medium distances to obstacles.","PeriodicalId":294701,"journal":{"name":"2015 IEEE Intelligent Vehicles Symposium (IV)","volume":"172 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116142435","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 : 2015-08-27DOI: 10.1109/IVS.2015.7225668
JeongMok Ha, Byeongchan Jeon, WooYeol Jun, Joonho Lee, Hong Jeong
In advanced driver assistance systems, the stereo matching algorithm is the key resource to obtain depth information of outdoor scenes. Semi-Global Matching (SGM) is currently the most efficient stereo matching algorithm for outdoor environments. However, because the number of pixels is large, SGM uses only a subset of them when estimating the disparity of a pixel. To overcome this limitation, Cost Aggregation Table (CAT) was proposed which uses two-dimensional cost aggregation so as to utilize whole image information. In this paper, we propose improved global 2D cost aggregation methods by loosening aggregation constraints. It aggregates every cost in the whole image to estimate each disparity. Although our method aggregates every cost in the image, the computational complexity is the same as that of SGM and CAT. The proposed cost aggregation method achieves superior disparity accuracy compared to the SGM.
{"title":"An improved 2D cost aggregation method for advanced driver assistance systems","authors":"JeongMok Ha, Byeongchan Jeon, WooYeol Jun, Joonho Lee, Hong Jeong","doi":"10.1109/IVS.2015.7225668","DOIUrl":"https://doi.org/10.1109/IVS.2015.7225668","url":null,"abstract":"In advanced driver assistance systems, the stereo matching algorithm is the key resource to obtain depth information of outdoor scenes. Semi-Global Matching (SGM) is currently the most efficient stereo matching algorithm for outdoor environments. However, because the number of pixels is large, SGM uses only a subset of them when estimating the disparity of a pixel. To overcome this limitation, Cost Aggregation Table (CAT) was proposed which uses two-dimensional cost aggregation so as to utilize whole image information. In this paper, we propose improved global 2D cost aggregation methods by loosening aggregation constraints. It aggregates every cost in the whole image to estimate each disparity. Although our method aggregates every cost in the image, the computational complexity is the same as that of SGM and CAT. The proposed cost aggregation method achieves superior disparity accuracy compared to the SGM.","PeriodicalId":294701,"journal":{"name":"2015 IEEE Intelligent Vehicles Symposium (IV)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116149726","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 : 2015-08-27DOI: 10.1109/IVS.2015.7225816
S. Formentin, Andrea G. Bianchessi, S. Savaresi
The free-floating car sharing model is a recently introduced vehicle rental model, which allows customers to return the car anywhere within the operation area, without relying on depot stations. Driven by the flexibility of such a model, the popularity of car sharing has increased rapidly during the last years. However, some critical issues still arise when a user needs to make plans of vehicle usage, since no information is available on future vehicle locations. In this paper, the Vehicle Distance Prediction (VDP) approach is proposed, aimed to predict the distance of the nearest available vehicle at a given future instant. This technique shows great potential also for the service manager, e.g. vehicles could be moved in advance by the staff to balance the fleet distribution. The effectiveness of the proposed prediction approach is assessed on a real dataset taken from a car sharing service in Milan, Italy.
{"title":"On the prediction of future vehicle locations in free-floating car sharing systems","authors":"S. Formentin, Andrea G. Bianchessi, S. Savaresi","doi":"10.1109/IVS.2015.7225816","DOIUrl":"https://doi.org/10.1109/IVS.2015.7225816","url":null,"abstract":"The free-floating car sharing model is a recently introduced vehicle rental model, which allows customers to return the car anywhere within the operation area, without relying on depot stations. Driven by the flexibility of such a model, the popularity of car sharing has increased rapidly during the last years. However, some critical issues still arise when a user needs to make plans of vehicle usage, since no information is available on future vehicle locations. In this paper, the Vehicle Distance Prediction (VDP) approach is proposed, aimed to predict the distance of the nearest available vehicle at a given future instant. This technique shows great potential also for the service manager, e.g. vehicles could be moved in advance by the staff to balance the fleet distribution. The effectiveness of the proposed prediction approach is assessed on a real dataset taken from a car sharing service in Milan, Italy.","PeriodicalId":294701,"journal":{"name":"2015 IEEE Intelligent Vehicles Symposium (IV)","volume":"53 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123787346","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 : 2015-08-27DOI: 10.1109/IVS.2015.7225758
S. Khastgir, S. Birrell, G. Dhadyalla, P. Jennings
Recently there has been a growth in the incorporation of autonomous features within vehicles. From being perceived as a comfort feature, autonomous features in vehicles have now become a safety feature which are foreseen to reduce accidents. This has led to a new trend within the automotive industry of focussing on autonomous features for driver safety, which might ultimately lead to fully autonomous vehicles. Considering the fact that most of the accidents on UK roads occur due to driver error, driver-less vehicles would prove to be a benefit. However with automation, an even greater challenge of system validation in all scenarios needs to be addressed. For this, various methods of validation have been developed by different research organizations and manufacturers, but a standardized process still evades the industry. Some of the existing methods have been discussed in this paper to critically compare their quality of results and ease of execution. Subsequently, a new test platform has been proposed using the 3xD driving simulator which encompasses most requirements of a general testing method. A standardized process which would benefit the industry both in terms of reducing costs of having varied processes, and by increasing customer confidence can be developed using a non-invasive platform like the 3xD driving simulator. The novelty of the 3xD simulator is the ability to drive-in any vehicle (production/prototype) and develop testing methodologies in an immersive wireless environment.
{"title":"Identifying a gap in existing validation methodologies for intelligent automotive systems: Introducing the 3xD simulator","authors":"S. Khastgir, S. Birrell, G. Dhadyalla, P. Jennings","doi":"10.1109/IVS.2015.7225758","DOIUrl":"https://doi.org/10.1109/IVS.2015.7225758","url":null,"abstract":"Recently there has been a growth in the incorporation of autonomous features within vehicles. From being perceived as a comfort feature, autonomous features in vehicles have now become a safety feature which are foreseen to reduce accidents. This has led to a new trend within the automotive industry of focussing on autonomous features for driver safety, which might ultimately lead to fully autonomous vehicles. Considering the fact that most of the accidents on UK roads occur due to driver error, driver-less vehicles would prove to be a benefit. However with automation, an even greater challenge of system validation in all scenarios needs to be addressed. For this, various methods of validation have been developed by different research organizations and manufacturers, but a standardized process still evades the industry. Some of the existing methods have been discussed in this paper to critically compare their quality of results and ease of execution. Subsequently, a new test platform has been proposed using the 3xD driving simulator which encompasses most requirements of a general testing method. A standardized process which would benefit the industry both in terms of reducing costs of having varied processes, and by increasing customer confidence can be developed using a non-invasive platform like the 3xD driving simulator. The novelty of the 3xD simulator is the ability to drive-in any vehicle (production/prototype) and develop testing methodologies in an immersive wireless environment.","PeriodicalId":294701,"journal":{"name":"2015 IEEE Intelligent Vehicles Symposium (IV)","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125101191","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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