Perception Methods for Adverse Weather Based on Vehicle Infrastructure Cooperation System: A Review
Environment perception plays a crucial role in autonomous driving technology. However, various factors such as adverse weather conditions and limitations in sensing equipment contribute to low perception accuracy and a restricted field of view. As a result, intelligent connected vehicles (ICVs) are...
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2024-01-01
|
| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/24/2/374 |
| _version_ | 1797339447958700032 |
|---|---|
| author | Jizhao Wang Zhizhou Wu Yunyi Liang Jinjun Tang Huimiao Chen |
| author_facet | Jizhao Wang Zhizhou Wu Yunyi Liang Jinjun Tang Huimiao Chen |
| author_sort | Jizhao Wang |
| collection | DOAJ |
| description | Environment perception plays a crucial role in autonomous driving technology. However, various factors such as adverse weather conditions and limitations in sensing equipment contribute to low perception accuracy and a restricted field of view. As a result, intelligent connected vehicles (ICVs) are currently only capable of achieving autonomous driving in specific scenarios. This paper conducts an analysis of the current studies on image or point cloud processing and cooperative perception, and summarizes three key aspects: data pre-processing methods, multi-sensor data fusion methods, and vehicle–infrastructure cooperative perception methods. Data pre-processing methods summarize the processing of point cloud data and image data in snow, rain and fog. Multi-sensor data fusion methods analyze the studies on image fusion, point cloud fusion and image-point cloud fusion. Because communication channel resources are limited, the vehicle–infrastructure cooperative perception methods discuss the fusion and sharing strategies for cooperative perception information to expand the range of perception for ICVs and achieve an optimal distribution of perception information. Finally, according to the analysis of the existing studies, the paper proposes future research directions for cooperative perception in adverse weather conditions. |
| first_indexed | 2024-03-08T09:48:17Z |
| format | Article |
| id | doaj.art-f0244f2e4c3a4368b36377ce6161a030 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-08T09:48:17Z |
| publishDate | 2024-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-f0244f2e4c3a4368b36377ce6161a0302024-01-29T14:13:52ZengMDPI AGSensors1424-82202024-01-0124237410.3390/s24020374Perception Methods for Adverse Weather Based on Vehicle Infrastructure Cooperation System: A ReviewJizhao Wang0Zhizhou Wu1Yunyi Liang2Jinjun Tang3Huimiao Chen4School of Mechanical Engineering, Xinjiang University, Urumqi 830017, ChinaSchool of Mechanical Engineering, Xinjiang University, Urumqi 830017, ChinaSchool of Traffic & Transportation Engineering, Central South University, Changsha 410075, ChinaSchool of Traffic & Transportation Engineering, Central South University, Changsha 410075, ChinaTsinghua Laboratory of Brain and Intelligence, Tsinghua University, Beijing 100084, ChinaEnvironment perception plays a crucial role in autonomous driving technology. However, various factors such as adverse weather conditions and limitations in sensing equipment contribute to low perception accuracy and a restricted field of view. As a result, intelligent connected vehicles (ICVs) are currently only capable of achieving autonomous driving in specific scenarios. This paper conducts an analysis of the current studies on image or point cloud processing and cooperative perception, and summarizes three key aspects: data pre-processing methods, multi-sensor data fusion methods, and vehicle–infrastructure cooperative perception methods. Data pre-processing methods summarize the processing of point cloud data and image data in snow, rain and fog. Multi-sensor data fusion methods analyze the studies on image fusion, point cloud fusion and image-point cloud fusion. Because communication channel resources are limited, the vehicle–infrastructure cooperative perception methods discuss the fusion and sharing strategies for cooperative perception information to expand the range of perception for ICVs and achieve an optimal distribution of perception information. Finally, according to the analysis of the existing studies, the paper proposes future research directions for cooperative perception in adverse weather conditions.https://www.mdpi.com/1424-8220/24/2/374ICVautonomous drivingadverse weather conditionsdata preprocessingmulti-sensor information fusionvehicle–infrastructure cooperation perception |
| spellingShingle | Jizhao Wang Zhizhou Wu Yunyi Liang Jinjun Tang Huimiao Chen Perception Methods for Adverse Weather Based on Vehicle Infrastructure Cooperation System: A Review Sensors ICV autonomous driving adverse weather conditions data preprocessing multi-sensor information fusion vehicle–infrastructure cooperation perception |
| title | Perception Methods for Adverse Weather Based on Vehicle Infrastructure Cooperation System: A Review |
| title_full | Perception Methods for Adverse Weather Based on Vehicle Infrastructure Cooperation System: A Review |
| title_fullStr | Perception Methods for Adverse Weather Based on Vehicle Infrastructure Cooperation System: A Review |
| title_full_unstemmed | Perception Methods for Adverse Weather Based on Vehicle Infrastructure Cooperation System: A Review |
| title_short | Perception Methods for Adverse Weather Based on Vehicle Infrastructure Cooperation System: A Review |
| title_sort | perception methods for adverse weather based on vehicle infrastructure cooperation system a review |
| topic | ICV autonomous driving adverse weather conditions data preprocessing multi-sensor information fusion vehicle–infrastructure cooperation perception |
| url | https://www.mdpi.com/1424-8220/24/2/374 |
| work_keys_str_mv | AT jizhaowang perceptionmethodsforadverseweatherbasedonvehicleinfrastructurecooperationsystemareview AT zhizhouwu perceptionmethodsforadverseweatherbasedonvehicleinfrastructurecooperationsystemareview AT yunyiliang perceptionmethodsforadverseweatherbasedonvehicleinfrastructurecooperationsystemareview AT jinjuntang perceptionmethodsforadverseweatherbasedonvehicleinfrastructurecooperationsystemareview AT huimiaochen perceptionmethodsforadverseweatherbasedonvehicleinfrastructurecooperationsystemareview |