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A Bayesian regression analysis of truck drivers' use of cooperative adaptive cruise control (CACC) for platooning on California highways
| 题名： | A Bayesian regression analysis of truck drivers' use of cooperative adaptive cruise control (CACC) for platooning on California highways |
| 正文语种： | eng |
| 作者： | Shiyan Yang;Steven E. Shladover;Xiao-Yun Lu;Hani Ramezani;Aravind Kailas;Osman D. Altan |
| 作者单位： | Partners for Advanced Transportation Technology (PATH) Institute of Transportation Studies University of California Berkeley Richmond CA USA;Partners for Advanced Transportation Technology (PATH) Institute of Transportation Studies University of California Berkeley Richmond CA USA;Partners for Advanced Transportation Technology (PATH) Institute of Transportation Studies University of California Berkeley Richmond CA USA;Partners for Advanced Transportation Technology (PATH) Institute of Transportation Studies University of California Berkeley Richmond CA USA;Volvo Group North America Costa Mesa CA USA;FHWA Washington D.C. USA||Global Smart System LLC Troy Ml USA |
| 关键词： | Bayesian regression model; CACC; time gap selection; traffic density; truck platooning |
| 摘要： | Cooperative Adaptive Cruise Control (CACC), as an advanced version of adaptive cruise control (ACC), automates brake and engine controls based on the information received from wireless V2V communications and remote sensors, enabling smaller vehicle-following time gaps. It can improve the safety of vehicle platooning and increase fuel savings. As an extension of our previous investigation of truck drivers' acceptance of CACC, this case study investigates factors affecting the use of CACC for truck platooning. Nine commercial fleet drivers were recruited to operate two following trucks in a CACC-enabled string on freeways in Northern California. We analyzed the usage of CACC time gaps and its correlation with truck drivers' stated preferences for these time gaps, and we found that the highest preferred Gap 3 (1.2 s) was used the most. Moreover, a Bayesian regression model was built to show that truck drivers are more likely to disengage CACC when driving in low-speed traffic or on downgrades where this CACC could not provide sufficient braking. In high-speed traffic or on upgrades, truck drivers are more likely to engage CACC, particularly at Gap 3. Truck position, however, does not affect truck drivers' time gap selection. The findings encourage the adoption of CACC in the trucking industry through implementing driver-preferred time gaps and responsive braking systems, and operating on routes with minimal interference to truck speeds. |
| 出版年： | 2023 |
| 期刊名称： | Journal of Intelligent Transportation Systems |
| 卷： | 27 |
| 期： | 1/6 |
| 页码： | 80-91 |
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