研究方向
  • 环境感知

研究多传感器协同感知及融合技术,实现对周围环境的关键信息收集和知识提取,其中包括可行驶区域、车道线、交通标志、交通灯、车辆、行人及所有障碍物的检测识别,车辆、行人等障碍物的位姿、运动状态等的预测;通过合理的多传感器布局配置方案,来确保车周围360度覆盖和关键区域的增强覆盖;通过安全冗余设计的软件系统方案来保障车辆周围环境信息的准确感知,进而确保自动驾驶汽车的安全稳定运行。

  • 高精定位

研究基于不同模态传感器的高精定位技术,如激光定位、视觉定位、组合导航定位,打造支持全天候、全场景的厘米级定位能力。建立面向自动驾驶应用的高精度建图体系,研发规模化、高效率的地图更新技术。探索基于数据驱动的定位全链路技术方案,通过海量的测试和运营数据提升算法的泛化能力。

  • 决策规划

接受定位、感知、预测的结果,基于海量实测与仿真的驾驶行为数据,学习最优的行为模型和参数,在高维空间中进行搜索和优化,理解周围行人车辆的意图,在交互的过程中完成符合社会参与者预期的安全、高效、稳定的运行轨迹,并最终通过运动底盘进行执行。

  • 智能控制

以车辆控制为主体,包含横纵向控制、安全机制两方面。横向控制主要研究自动驾驶的路径跟踪能力,依据运动规划输出的路径、曲率等信息及有效的控制参数模型估计进行跟踪控制,以减少跟踪误差,同时保证行驶的稳定性。纵向控制主要是控制车辆速度跟踪能力,实现对期望车速的精确跟随,按照预定的速度巡航。建立全面的上下游安全机制,具备异常处理的能力,保证控制精度和场景安全需求的最优化。

  • 仿真平台

通过对真实的环境进行建模,真实的车辆进行建模,真实的交互行为进行建模,提供逼真的数字仿真世界。解决真实世界中的测试里程不足、测试场景丰富度欠缺、测试成本高等难题;也为算法迭代提供海量的训练数据和安全的评测环境,助力无人驾驶算法快速迭代优化,保证无人驾驶系统快速验证发布。

  • 数据平台

建设无人驾驶数据平台,提供数据筛选、传输、存储、挖掘、分析、标注等功能。为研发、测试、产品、运营人员提供快速方便的数据检索服务;针对数据进行高阶语义建模,挖掘对无人驾驶算法演进最有价值的复杂交互场景数据,助力算法快速迭代优化;通过数据分析,快速定位无人驾驶的重点问题,并提供深度分析,为分析问题、解决问题、制定方案决策提供数据支持。


产品及应用
  • 智能物流机器人

    研发落地末端智能物流机器人,将自动驾驶技术与物流揽收场景、即时配送场景进行结合,实现智能、高效、安全、环保的货物流转与投递。目前已经在国内多个城市的校园、社区、园区进行常态化运营,解决物流的最后三公里问题,实现末端无接触配送。未来,随着技术的不断发展,将产品延伸到公开道路,把公开道路的物流无人车与末端物流机器人协同,实现全链路无人化的智慧新物流体系。

    了解更多 >


研究团队
沈加翔

阿里巴巴集团高级研究员,1999年硕士毕业于中科院计算所,2008年加入阿里,历时12年带领搜索/推荐/广告工程走向统一的智能引擎中台(AI·OS),随后在电商领域推动业务Serverless平台建设,促进资源和研发效能与AI·OS的体系融合。现任达摩院自动驾驶实验室负责人,秉持autodrive平台与人才相互成就的理念,打造物流领域自动驾驶核心竞争力。


学术成果
论文和学术报告
  • H Ding, X Jiang, B Shuai, AQ Liu, G Wang. Semantic segmentation with context encoding and multi-path decoding. TIP, 2020.
  • C Lin, J Lu, G Wang, J Zhou. Graininess-aware deep feature learning for robust pedestrian detection. TIP, 2020.
  • K Yuan, Z Guo, and ZJ Wang. RGGNet: Tolerance Aware LiDAR-Camera Online Calibration With Geometric Deep Learning and Generative Model . RAL, 2020.
  • M Zhang, X Xu, Y Chen, M Li. A Lightweight and Accurate Localization Algorithm Using Multiple Inertial Measurement Units. RAL, 2020.
  • M Zhang, X Xu, Y Chen, M Li. A Lightweight and Accurate Localization Algorithm Using Multiple Inertial Measurement Units. RAL, 2020.
  • J Liu, A Shahroudy, ML Perez, G Wang, LY Duan, AK Chichung. NTU RGB+D 120: A large-scale benchmark for 3d human activity understanding. TPAMI, 2019.
  • J Liu, A Shahroudy, G Wang, LY Duan, AK Chichung. Skeleton-based online action prediction using scale selection network. TPAMI, 2019.
  • J Liu, H Ding, A Shahroudy, LY Duan, X Jiang, G Wang. AK Chichung Feature boosting network for 3D pose estimation. TPAMI, 2019.
  • H Ding, X Jiang, B Shuai, AQ Liu, G Wang. Semantic correlation promoted shape-variant context for segmentation. CVPR, 2019.
  • J Gu, S Joty, J Cai, H Zhao, X Yang, G Wang. Unpaired image captioning via scene graph alignments. ICCV, 2019.
  • Jiuxiang Gu, Jianfei Cai, Shafiq Joty, Li Niu, Gang Wang. Look, Imagine and Match: Improving Textual-Visual Cross-Modal Retrieval with Generative Models. CVPR, 2018. Spotlight
  • Ping Hu, Gang Wang, Xiangfei Kong, Jason Kuen,Yap-Peng Tan. Motion-Guided Cascaded Refinement Network for Video Object Segmentation. CVPR, 2018. Poster
  • Jason Kuen, Xiangfei Kong, Zhe Lin, Gang Wang, Jianxiong Yin, Simon See, Yap-Peng Tan. Stochastic Downsampling for Cost-Adjustable Inference and Improved Regularization in Convolutional Networks. CVPR, 2018. Poster
  • Jianlou Si, Honggang Zhang, Chun-Guang Li, Jason Kuen, Xiangfei Kong, Alex C. Kot, Gang Wang. Dual Attention Matching Network for Context-Aware Feature Sequence based Person Re-Identifcation. CVPR, 2018. Poster
  • Jun Liu, Amir Shahroudy, Gang Wang, Ling-Yu Duan, Alex C. Kot. SSNet: Scale Selection Network for Online 3D Action Prediction. CVPR, 2018. Spotlight
  • Henghui Ding, Xudong Jiang, Bing Shuai, Ai Qun Liu, Gang Wang. Context Contrasted Feature and Gated Multi-scale Aggregation for Scene Segmentation. CVPR, 2018. Oral
  • Yicheng Wang, Zhenzhong Chen, Feng Wu, Gang Wang. Person Re-identification with Cascaded Pairwise Convolutions. CVPR, 2018. Poster
  • Lu Zhang, Ju Dai, Huchuan Lu, You He, Gang Wang. A Bi-directional Message Passing Model for Salient Object Detection. CVPR, 2018. Poster
  • Xiaoning Zhang, Tiantian Wang, Jinqing Qi, Huchuan Lu, Gang Wang. Progressive Attention Guided Recurrent Network for Salient Object Detection.CVPR, 2018. Poster
  • Jiuxiang Gu, Jianfei Cai, Gang Wang, Tsuhan Chen. Stack-Captioning: Coarse-to-Fine Learning for Image Captioning. AAAI, 2018. Oral
  • Rana Hanocka, Noa Fish, Zhenhua Wang, Raja Giryes, Shachar Fleishman, Daniel Cohen-Or. ALIGNet: Partial-Shape Agnostic Alignment via Unsupervised Learning. ACM Transactions on Graphics (TOG),2018.
  • Bin Wang, Guofeng Wang, Andrei Sharf, Yangyan Li, Fan Zhong, Xueying Qin, Daniel Cohen-Or, Baoquan Chen. Active Assembly Guidance with Online Video Parsing. IEEE VR, 2018.
  • Gang Zhang, Hu Han, Shiguang Shan, Xingguang Song, Xilin Chen. Face Alignment across Large Pose via MT-CNN based 3D Shape Reconstruction. FG, 2018.
  • Gang Zhang, Meina Kan, Shiguang Shan, Xilin Chen. Generative Adversarial Network with Spatial Attention for Face Attribute Editing. ECCV, 2018.
  • Jun Liu and Gang Wang. Global Context-Aware LSTM Networks for 3D Action Recognition. CVPR, 2017.
  • Ping Hu, Bing Shuai, Gang Wang. Deep Level Sets for Salient Object Detection. CVPR, 2017.
  • Abrar Abdulnabi, Bing Shuai, Gang Wang. Episodic CAMN: Contextual Attention-based Memory Networks for Scene Labeling. CVPR, 2017.
  • Jiuxiang Gu, Gang Wang, Jianfei Cai, and Tsuhan Chen. An Empirical Study of Language CNN for Image Captioning. ICCV, 2017.
  • Zhenhua Wang, Jiuxiang Gu, Jason Kuen, Lianyang Ma, Amir Shahroudy, Bing Shuai, Ting Liu, Xingxing Wang and Gang Wang. Recent Advances in Convolutional Neural Networks. Pattern Recognition (PR), 2017.
  • Zhenhua Wang, Xingxing Wang, Gang Wang. Learning Fine-grained features via a CNN tree for Large-scale Classification. Neurocomputing, 2017.
  • Zhenwei Miao, Kim-Hui Yap, Xudong Jiang, Subbhuraam Sinduja, Zhenhua Wang. Laplace Gradient based Discriminative and Contrast Invertible Descriptor. ICASSP, March 2017.
  • Amir Shahroudy, Tian-Tsong Ng, Yihong Gong, and Gang Wang. Deep Multimodal Feature Analysis for Action Recognition in RGB+D Videos. IEEE.
  • Mingliang Chen, Qingxiong Yang, Qing Li, Gang Wang, and Ming-Hsuan Yang. Spatiotemporal Background Subtraction. IEEE.
  • Bing Shuai, Zhen Zuo, Bing Wang, ang Gang Wang. Scene Segmentation with DAG-Recurrent Neural Networks. IEEE.
  • Jun Liu, Amir Shahroudy, Dong Xu, Alex Kot Chichung, and Gang Wang. Skeleton-Based Action Recognition Using Spatio-Temporal LSTM Network with Trust Gates. IEEE.
展开更多

联系我们
E-mail: candice.ytt@alibaba-inc.com

扫描二维码
关注阿里技术微信公众号