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交通数字孪生、具身系统测试场景生成与在环自主测评。课题组条件1.充足的纵向项目经费支撑（重点研发\u002F重点基金等纵向项目）2.算力平台和实操环境支撑（近百张高性能显卡及多类具身实验平台）3.培养路径制定（研究方向、论文以及代码指导、传帮带、会议资助）4.就业单位推荐（高等院校、科研院所、IT大厂）对学生的要求1.求知若渴，视学习为改变命运的核心动力；2.心态阳光，坚信办法总比困难多；3.基础扎实，具备独立分析与解决问题的底层能力；4.执行力强，愿意相信“先完成，再完美”的行动哲学。项目案例展示（1）无监督表征增强视觉识别面向复杂动态降质环境中视觉识别鲁棒性挑战，提出了一种无监督表征增强方法。基于深度通道先验，构建一个“即插即用”的无监督特征增强模块，从稀疏特征分布中提取紧凑且有效的特征表示，解决了图像中的空间稀疏性和通道差异性问题。在浅层表征空间中引入双学习架构，并通过两阶段的多对抗学习机制进行无监督特征校正，显著提升了现有基线模型在降质图像上的识别性能。（2）基于事件相机的高效交通目标检测针对事件流数据的高度空间稀疏和低信噪比特性，提出了基于稀疏Mamba的事件目标检测框架SMamba。基于活动事件和噪声事件的时空分布差异，设计了时空连续性评估模块以评估不同区域的信息含量，并在计算过程中自适应丢弃无事件和噪声区域；构建了信息量引导的空间扫描机制和基于全局空间的通道扫描机制，增强了模型的全局建模能力，在精度和效率之间达到了更好的平衡。（3）自动驾驶动态交通环境全要素感知面向开放域交通环境下稳健感知的挑战，提出了多任务层级联合的智能汽车复杂动态环境视觉解析框架，解决了远距离目标的精准检测、低感知精度下高相似物体的鲁棒识别和非一致场景下的多目标实时跟踪等问题，实现了可见光下交通场景全要素及可通行区域的精准感知；采用多传感器配置，攻克了多模态、多尺度数据帧间时空特征配准难的问题，构建了一种多模态融合的三维目标鲁棒检测方法，提高了复杂动态环境下全天候关键目标解析的鲁棒性。该技术应用于长安大学自主研发的信达号和知行合一无人车，斩获世界智能驾驶挑战赛实车赛与虚拟仿真赛道技术创新奖、争先奖及国家奖项5项；全国大学生工程训练综合能力竞赛智能汽车仿真赛项全国二等奖、省级特等奖4项。（4）复杂动态交通场景车辆微观轨迹预测面向真实交通场景中动静态目标遮挡、感知算法失效及感知范围限制带来的非完整轨迹预测挑战，构建了多尺度注意力头，从不同时间颗粒度并行提取轨迹的多尺度运动表征，缓解了缺失值对轨迹预测带来的负面影响；提出了连续性表征引导的多尺度融合模块，跨时间步计算轨迹的连续性表征，并将其用于运动表征的多尺度融合，极大地太提高了模型对非完整轨迹的预测精度，使模型能够更鲁棒的应用于真实交通场景。（a）模型框架图（b）预测结果可视化（5）城市交叉路口全息感知与数字孪生面向城市交叉路口交通场景全息感知与动态建模的挑战，提出了真实道路布局语义及参数提取与路段级-车道级运动轨迹流预测方法，基于三维仿真工具建立了道路场景的三维虚拟实体道路，实现了城市交叉口实时提取的轨迹流数据和相应的预测轨迹数据还原到实际交通运行环境的虚拟实体中，准确地描述中微观层面动态交通场景流数字孪生与模拟推演，使用轨迹预测结果填补了数字孪生物理实体到虚拟环境映射时延。具身实验平台展示①长安大学车联网与智能汽车试验场以及对应的数字孪生平台②智能网联测试车③可移动智能路侧杆④长安大学车联网与智能汽车试验场微缩仿真平台以及对应的数字孪生平台⑤系列微型无人系统⑥虚拟仿真平台往届毕业生去向百度，钉钉，法士特，长鑫科技，极氪汽车，西安建筑科技大学，军工研究所（保密单位），山东高速，北京万集，陕汽，腾讯（实习），字节跳动（实习）","刘占文，女，工学博士，三级教授，博士生导师，国家级青年人才，交通运输行业青年拔尖人才，现任长安大学机器人与具身智能学院副院长、深圳研究院副院长、陕西省车联网与智能汽车测试技术工程研究中心副主任、长安大学交通信息检测与控制工程实验室主任。研究方向聚焦于智能网联交通、车路具身系统及自主测试。以第一\u002F通讯作者在AAAI、ECCV、TMM、CommTR等国际权威刊物及顶级会议发表SCI论文30余篇，授权国际\u002F国家发明专利51项，参与制定多项国家及团体标准。主持国家重点研发计划项目课题、国家自然科学基金重点、面上及青年项目、陕西省“两链”融合重点专项等国家级、省部级科研项目20余项。获第十四届陕西省青年科技奖、陕西省科学技术进步奖一等奖3项、陕西省高等教育教学成果奖特等奖1项、中国公路学会科学技术奖一等奖、二等奖各1项。 \n                                \n                                \n                                    \n                                         社会职务 \n                                    \n                                     1、期刊任职（1）《交通运输工程学报》青年编委；（2）《Journal of Intelligent and Connected Vehicles》Associate Editor；2、会议任职（1）IEEE智能交通系统国际会议青年学术委员会联合主席；（2）IEEE交通信息与安全国际会议执行副主席及论坛主席；（3）WTC第三届学部委员会交通工程学部交通感知与大数据学科交通系统智能感知技术委员会主席；（4）WTC第三届学部委员会公路工程学部智慧公路学科自动驾驶车协作技术委员会副主席；（5）IEEE机械与电子工程国际会议程序委员会联席主席；3、学会任职（1）中国图象图形学会视觉传感专业委员会委员；（2）中国汽车工程学会人工智能分会委员；（3）中国仿真学会数字试验与测试分委会理事；（4）中国公路学会自动驾驶工作委员会委员","http:\u002F\u002Fjs.chd.edu.cn\u002Fdzykzgcxy\u002Flzw\u002Flist.htm",[],[],{"id":412,"name":413,"orgName":414,"school":389,"gender":390,"title":391,"degree":392,"honor":10,"position":10,"researchDirection":415,"intro":416,"province":395,"completeness":417,"avatarAuthorized":396,"sourceUrl":418,"nameValid":398,"tags":419,"tagNames":420},"f708985b-bf57-6f43-8c5f-cda897f75fd0","马骏驰","长安大学信息工程学院","1.软错误传播评估研究软错误（半导体电路瞬态故障）是影响计算可靠性的重要问题。如何评估软错误传播是软错误防护面临的最重要的挑战。提出利用表示学习方法高效地提取程序语义并使用于传播分析。 2.电动车充电调度研究缓慢的充电速度是影响电动汽车用户体验的重要因素。如何根据订单的特征设计高效的充电协同策略以最大化收益是电动车调度的难点。提出了利用深度强化学习从与环境的交互中学到深层的语义信息，避免短视的决策，所提出模型可显著减少用户等待时间和充电时间代价。 3.单细胞多模态融合研究单细胞测序技术能够对单个细胞的基因和染色质可及性等特征进行高通量、高精度的检测,从而获得了细胞内多组学的海量数据。然而,如何融合单细胞多模态数据以支撑日益增长的以单细胞为基础的医学分析需求,进一步为阐明基因层面相关疾病的发生发展机理,仍然是一个亟待解决的关键科学问题。提出基于图神经网络的多模态融合方法，更好地实现多模态数据的语义特征提取，为下游分析任务提供强有力的支持","马骏驰，男，1988年出生，博士，副教授，硕士生导师。2017年获东南大学计算机应用技术博士学位。2017年进入长安大学任教师。在科学研究方面，专注于软件可靠性和电动汽车调度的研究。主持国家自然科学基金青年项目一项，主持陕西省重点研发计划一般项目一项。在国内外顶级刊物和国际会议上发表SCI\u002FEI论文10余篇。应邀在ASP-DAC和IEEE VLSI Test Symposium等国际重要学术会议（CCF C类会议）作分会报告，此外还担任了IEEE ACCESS和Journal of Electronic Testing审稿人，担任中国计算机协会西安分部委员。指导硕士研究生毕业6人，授权专利和软件著作权各2项。 \n                                \n                                \n                                    \n                                         社会职务 \n                                    \n                                     CCF西安分部委员、ACM会员",95,"http:\u002F\u002Fjs.chd.edu.cn\u002Fxxgcxy\u002Fmjc\u002Flist.htm",[],[],{"id":422,"name":423,"orgName":424,"school":389,"gender":390,"title":425,"degree":392,"honor":10,"position":10,"researchDirection":426,"intro":427,"province":395,"completeness":95,"avatarAuthorized":396,"sourceUrl":428,"nameValid":398,"tags":429,"tagNames":430},"de821f4c-c35a-5ec7-2061-ce19a58a8048","袁博","长安大学数据科学与人工智能研究院","讲师","人工智能与交通基础设施智能感知• 智能视觉感知、多模态融合与跨场景学习• 道路与机场道面病害智能检测与量化• 路面三维多视图成像、点云处理与结构病害诊断• 交通基础设施性能评估与智能养护决策• 地下空间与道路隐蔽病害智能探测","袁博，博士研究生，数据科学与人工智能研究院专任教师、硕士研究生导师，长安大学——中创云图地下空间安全数智化联合研究中心副主任。主要从事人工智能与交通基础设施智能感知交叉研究，重点关注多模态视觉感知、道路与机场道面智能检测、地下空间安全数智化、三维视觉以及智能养护决策等方向。先后参与国家重点研发计划2项、国家自然科学基金及省部级科研项目等10余项，主持陕西省自然科学基础研究计划（青年）项目、陕西省教育厅服务地方专项（政企联合自主项目）和中央高校基本科研业务费基础研究培育项目等。共发表国内外学术论文18篇，其中SCI\u002FSCIE收录9篇、会议论文7篇、中文期刊论文2篇；研究成果涵盖机场与道路病害智能检测、多模态融合、三维数据处理与智能交通等主题。作为发明人参与多项专利成果，已获授权专利和软件著作权5项。获2025年长安大学优秀博士学位论文、2025年全国高校人工智能教育大会优秀论文特等奖；参与成果获2025年度陕西省交通运输科学技术奖一等奖、2025年度茅以升科学技术奖—交通运输科学技术奖二等奖等。指导学生获得中国研究生智慧城市技术与创意设计大赛全国一等奖、智慧交通创新大赛国家级一等奖等竞赛奖励。中国公路学会会员，担任 IET Intelligent Transport Systems、Automation in Construction 等期刊及 Transportation Research Board Annual Meeting 等学术会议审稿人。 \n                                \n                                \n                                    \n                                         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