1 |
董汉, 舒伟, 陈超, 等. 危险驾驶工况场景的复杂度评估方法研究[J]. 汽车工程, 2020, 42(6):808-814.
|
|
DONG H, SHU W, CHEN C, et al. Research on complexity evaluation method of dangerous driving scenes[J]. Automotive Engineering, 2020, 42(6):808-814.
|
2 |
GAO F, DUAN J, HAN Z, et al. Automatic virtual test technology for intelligent driving systems considering both coverage and efficiency[J]. IEEE Transactions on Vehicular Technology, 2020,69(12):14365-14376.
|
3 |
武彪, 朱西产, 廖茂竹, 等. 路口车辆冲突与碰撞的安全边界条件模型[J]. 天津师范大学学报(自然科学版), 2019, 39(2):62-66.
|
|
WU B, ZHU X C, LIAO M Z, et al. Safety boundary condition model based on vehicle intersection conflict and collision[J]. Journal of Tianjin Normal University(Natural Science Edition), 2019, 39(2):62-66.
|
4 |
郭景华, 李克强, 王进, 等. 基于危险场景聚类分析的前车随机运动状态预测研究[J]. 汽车工程, 2020, 42(7):847-853.
|
|
GUO J H, LI K Q, WANG J, et al. Study on prediction of preceding vehicle’s stochastic motion based on risk scenarios clustering analysis[J]. Automotive Engineering, 2020, 42(7):847-853.
|
5 |
朱冰, 张培兴, 赵健, 等. 面向多维度逻辑场景的自动驾驶安全性聚类评价方法[J]. 汽车工程, 2020, 42(11):1458-1463.
|
|
ZHU B, ZHANG P X, ZHAO J, et al. Clustering evaluation method of autonomous driving safety for multi-dimension logical scenario[J]. Automotive Engineering, 2020, 42(11):1458-1463.
|
6 |
李江坤, 邓伟文, 任秉韬, 等. 基于场景动力学和强化学习的自动驾驶边缘测试场景生成方法[J]. 汽车工程, 2022, 44(7):976-986.
|
|
LI J K, DENG W W, REN B T, et al. Automatic driving edge scene generation method based on scene dynamics and reinforcement learning[J]. Automotive Engineering, 2022, 44(7):976-986.
|
7 |
严慈磊, 应朝阳, 孙巍, 等. 面向机非混行环境下的自动驾驶汽车测试场景构建方法研究[C]. 2020第十五届中国智能交通年会论文集, 2020:214-219.
|
|
YAN C L, YING Z C, SUN W, et al. Research on the construction method of intelligent connected vehicle test scenario in machine-to-miscellaneous environment[C]. 2020 Proceedings of the 15th China Intelligent Transportation Annual Conference, 2020:214-219.
|
8 |
PARK J, WEN M, SUNG Y, et al. Multiple event-based simulation scenario generation approach for autonomous vehicle smart sensors and devices.[J]. Multidisciplinary Digital Publishing Institute, 2019(20):4456-4469.
|
9 |
马凯. 典型潜在危险驾驶情景研究[D]. 长春:吉林大学,2017.
|
|
MA K. Research on typical potential dangerous driving scenarios[D]. Changchun:Jinlin University, 2017.
|
10 |
盛彬. 基于信息融合的驾驶危险场景分析识别研究[D]. 哈尔滨:哈尔滨工业大学,2019.
|
|
SHENG B. Research on analysis and identification of driving dangerous scenarios based on information fusion[D]. Harbin: Harbin Institute of Technology, 2019.
|
11 |
曾宇凡,朱西产, 马志雄, 等. 基于DREAM方法的追尾危险场景诱导因素分析[C].国际汽车交通安全学术会议, 2018, 102:8-19.
|
|
ZENG Y F, ZHU X C, MA Z X, et al. Analysis of inducement factors of rear-end collision risk scenario based on DREAM method[C].International Conference on Automobile Traffic Safety, 2018, 102:8-19.
|
12 |
ZHAO D, HUANG X, PENG H, et al. Accelerated evaluation of automated vehicles in car-following maneuvers[J]. IEEE Transactions on Intelligent Transportation Systems, 2016:1-12.
|
13 |
KOREN M, ALSAIF S, LEE R, et al. Adaptive stress testing for autonomous vehicles[C]. IEEE Intelligent Vehicles Symposium. IEEE, 2019:234-240.
|
14 |
侯彦巧. 基于典型场景的自动紧急制动系统对骑车人保护效果研究[D]. 成都:西华大学, 2020.
|
|
HOU Y Q. Research on the protetion effect of automatic emergency braking system for riders based on typical scenario[D]. Chengdu:Xihua University, 2020.
|
15 |
黄璐. 基于本体论的无人驾驶车辆场景评估与行为决策方法研究[D]. 合肥:中国科学技术大学, 2019.
|
|
HUANG L. Research on ontology-based situation assessment and decision-making approach for autonomous vehicles[D]. Hefei:University of Science and Technology of China, 2019.
|
16 |
贾程栋. 面向城市智能汽车的场景多模真实感重建真实感重建技术研究[D]. 成都:电子科技大学, 2020.
|
|
JIA C D. Research on multi-mode photorealistic reconstruction technology for urban smart car[D]. Chengdu:University of Electronic Science and Technology of China, 2020.
|
17 |
KLISCHAT M, ALTHOFF M. Generating critical test scenarios for automated vehicles with evolutionary algorithms[C].2019 IEEE Intelligent Vehicles Symposium (IV), 2019: 2352-2358.
|
18 |
NAN Z, FENG Y, HE J, et al. Scene-guided region proposal re-ranking method for on-road vehicle candidate generation[C]. Proceedings of the 2019 IEEE Intelligent Vehicles Symposium (IV), IEEE, 2019: 2377-2382.
|
19 |
FENG S, FENG Y, YU C, et al. Testing scenario library generation for connected and automated vehicles, part I: methodology[J]. IEEE Transactions on Intelligent Transportation Systems, 2021, 22:1573-1582.
|
20 |
李江坤, 邓伟文, 任秉韬, 等. 一种智能汽车测试场景复杂度的评估方法[C]. 2020中国汽车工程学会年会论文集, 2020: 106-113.
|
|
LI J K, DENG W W, REN B T, et al. An evaluation method of test scenario complexity for intelligent vehicles[C]. SAECCE2020-ICV051, 2020: 106-113.
|
21 |
BARANYI P. Convex hull generation methods for polytopic representations of LPV models[C]. Proceedings of the 2009 7th International Symposium on Applied Machine Intelligence and Informatics, 2009: 69-74.
|