Quantification and Pictorial Expression of Driving Status Domain Boundaries for Autonomous Vehicles in LTAP/OD Scenarios
The ability of advanced driver assistance systems (ADAS) and autonomous vehicles to make human-like decisions can be enhanced by providing more detailed information about vehicles and in-vehicle users’ states. In this paper, the driving status domains of vehicles in left turn across path/opposite di...
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2023-07-01
|
| Series: | World Electric Vehicle Journal |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2032-6653/14/7/187 |
| _version_ | 1797587182338179072 |
|---|---|
| author | Xuan Ren Huanhuan Zhang Xiaolan Wang Weiwei Zhang Wangpengfei Yu |
| author_facet | Xuan Ren Huanhuan Zhang Xiaolan Wang Weiwei Zhang Wangpengfei Yu |
| author_sort | Xuan Ren |
| collection | DOAJ |
| description | The ability of advanced driver assistance systems (ADAS) and autonomous vehicles to make human-like decisions can be enhanced by providing more detailed information about vehicles and in-vehicle users’ states. In this paper, the driving status domains of vehicles in left turn across path/opposite direction (LTAP/OD) scenarios are subdivided into comfort, discomfort, extreme, and crash, and the boundaries of each status domain are quantified and visualized. First, real unprotected left turn road segments are chosen for the actual vehicle testing. Subjective passenger comfort evaluation results and objective motion state data of vehicles during the experiment are organized and analyzed by statistics. In addition, the pictorials are plotted to determine the comfort and extreme status domain boundaries based on motion state parameters. Second, based on the unprotected left turn kinematic analysis and modeling, as well as a skilled driver risk perception and operational model, the Safe Collision Plots (SCP) of conflicting vehicles in LTAP/OD scenarios are quantified and expressed as pictorial examples. By combining objective motion parameters and passenger experience, intuitively quantifying each driving status domain of vehicles can provide more fine-grained information for the design parameters of ADAS and autonomous vehicles and increase public trust and acceptance of them. |
| first_indexed | 2024-03-11T00:33:32Z |
| format | Article |
| id | doaj.art-b1868f5c0a324a01a663d4abc9cb177b |
| institution | Directory Open Access Journal |
| issn | 2032-6653 |
| language | English |
| last_indexed | 2024-03-11T00:33:32Z |
| publishDate | 2023-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | World Electric Vehicle Journal |
| spelling | doaj.art-b1868f5c0a324a01a663d4abc9cb177b2023-11-18T21:49:01ZengMDPI AGWorld Electric Vehicle Journal2032-66532023-07-0114718710.3390/wevj14070187Quantification and Pictorial Expression of Driving Status Domain Boundaries for Autonomous Vehicles in LTAP/OD ScenariosXuan Ren0Huanhuan Zhang1Xiaolan Wang2Weiwei Zhang3Wangpengfei Yu4School of Mechanical and Automotive Engineering, Shanghai University of Engineering Science, Shanghai 201620, ChinaSchool of Mechanical and Automotive Engineering, Shanghai University of Engineering Science, Shanghai 201620, ChinaSchool of Mechanical and Automotive Engineering, Shanghai University of Engineering Science, Shanghai 201620, ChinaShanghai Smart Vehicle Cooperating Innovation Center Co., Ltd., Shanghai 201805, ChinaShanghai Smart Vehicle Cooperating Innovation Center Co., Ltd., Shanghai 201805, ChinaThe ability of advanced driver assistance systems (ADAS) and autonomous vehicles to make human-like decisions can be enhanced by providing more detailed information about vehicles and in-vehicle users’ states. In this paper, the driving status domains of vehicles in left turn across path/opposite direction (LTAP/OD) scenarios are subdivided into comfort, discomfort, extreme, and crash, and the boundaries of each status domain are quantified and visualized. First, real unprotected left turn road segments are chosen for the actual vehicle testing. Subjective passenger comfort evaluation results and objective motion state data of vehicles during the experiment are organized and analyzed by statistics. In addition, the pictorials are plotted to determine the comfort and extreme status domain boundaries based on motion state parameters. Second, based on the unprotected left turn kinematic analysis and modeling, as well as a skilled driver risk perception and operational model, the Safe Collision Plots (SCP) of conflicting vehicles in LTAP/OD scenarios are quantified and expressed as pictorial examples. By combining objective motion parameters and passenger experience, intuitively quantifying each driving status domain of vehicles can provide more fine-grained information for the design parameters of ADAS and autonomous vehicles and increase public trust and acceptance of them.https://www.mdpi.com/2032-6653/14/7/187ADASautonomous vehiclesdriving status domainmotion state parameterspassenger experienceboundary quantification |
| spellingShingle | Xuan Ren Huanhuan Zhang Xiaolan Wang Weiwei Zhang Wangpengfei Yu Quantification and Pictorial Expression of Driving Status Domain Boundaries for Autonomous Vehicles in LTAP/OD Scenarios World Electric Vehicle Journal ADAS autonomous vehicles driving status domain motion state parameters passenger experience boundary quantification |
| title | Quantification and Pictorial Expression of Driving Status Domain Boundaries for Autonomous Vehicles in LTAP/OD Scenarios |
| title_full | Quantification and Pictorial Expression of Driving Status Domain Boundaries for Autonomous Vehicles in LTAP/OD Scenarios |
| title_fullStr | Quantification and Pictorial Expression of Driving Status Domain Boundaries for Autonomous Vehicles in LTAP/OD Scenarios |
| title_full_unstemmed | Quantification and Pictorial Expression of Driving Status Domain Boundaries for Autonomous Vehicles in LTAP/OD Scenarios |
| title_short | Quantification and Pictorial Expression of Driving Status Domain Boundaries for Autonomous Vehicles in LTAP/OD Scenarios |
| title_sort | quantification and pictorial expression of driving status domain boundaries for autonomous vehicles in ltap od scenarios |
| topic | ADAS autonomous vehicles driving status domain motion state parameters passenger experience boundary quantification |
| url | https://www.mdpi.com/2032-6653/14/7/187 |
| work_keys_str_mv | AT xuanren quantificationandpictorialexpressionofdrivingstatusdomainboundariesforautonomousvehiclesinltapodscenarios AT huanhuanzhang quantificationandpictorialexpressionofdrivingstatusdomainboundariesforautonomousvehiclesinltapodscenarios AT xiaolanwang quantificationandpictorialexpressionofdrivingstatusdomainboundariesforautonomousvehiclesinltapodscenarios AT weiweizhang quantificationandpictorialexpressionofdrivingstatusdomainboundariesforautonomousvehiclesinltapodscenarios AT wangpengfeiyu quantificationandpictorialexpressionofdrivingstatusdomainboundariesforautonomousvehiclesinltapodscenarios |