Na Du, Jinyong Kim, Feng Zhou, Elizabeth M. Pulver, D. Tilbury, L. Robert, A. Pradhan, Jessie Yang
{"title":"Evaluating Effects of Cognitive Load, Takeover Request Lead Time, and Traffic Density on Drivers’ Takeover Performance in Conditionally Automated Driving","authors":"Na Du, Jinyong Kim, Feng Zhou, Elizabeth M. Pulver, D. Tilbury, L. Robert, A. Pradhan, Jessie Yang","doi":"10.1145/3409120.3410666","DOIUrl":"https://doi.org/10.1145/3409120.3410666","url":null,"abstract":"In conditionally automated driving, drivers engaged in non-driving related tasks (NDRTs) have difficulty taking over control of the vehicle when requested. This study aimed to examine the relationships between takeover performance and drivers’ cognitive load, takeover request (TOR) lead time, and traffic density. We conducted a driving simulation experiment with 80 participants, where they experienced 8 takeover events. For each takeover event, drivers’ subjective ratings of takeover readiness, objective measures of takeover timing and quality, and NDRT performance were collected. Results showed that drivers had lower takeover readiness and worse performance when they were in high cognitive load, short TOR lead time, and heavy oncoming traffic density conditions. Interestingly, if drivers had low cognitive load, they paid more attention to driving environments and responded more quickly to takeover requests in high oncoming traffic conditions. The results have implications for the design of in-vehicle alert systems to help improve takeover performance.","PeriodicalId":373501,"journal":{"name":"12th International Conference on Automotive User Interfaces and Interactive Vehicular Applications","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131705498","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":"Decoding CNN based Object Classifier Using Visualization","authors":"Abhishek Mukhopadhyay, Imon Mukherjee, P. Biswas","doi":"10.1145/3409251.3411721","DOIUrl":"https://doi.org/10.1145/3409251.3411721","url":null,"abstract":"This paper investigates how working of Convolutional Neural Network (CNN) can be explained through visualization in the context of machine perception of autonomous vehicles. We visualize what type of features are extracted in different convolution layers of CNN that helps to understand how CNN gradually increases spatial information in every layer. Thus, it concentrates on region of interests in every transformation. Visualizing heat map of activation helps us to understand how CNN classifies and localizes different objects in image. This study also helps us to reason behind low accuracy of a model helps to increase trust on object detection module.","PeriodicalId":373501,"journal":{"name":"12th International Conference on Automotive User Interfaces and Interactive Vehicular Applications","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129399307","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}