
团队依托数字模拟技术提前识别道路隐患,为山区公路安全整治提供技术支撑
长安大学“安行智构”团队研发国产化公路安全仿真平台,通过数字模拟技术在设计阶段提前识别山区公路隐患,避免实地测试安全风险。该平台已在雅西高速51公里连续长下坡路段落地,实现年度零伤亡。
AI-generated summary
山区公路地形复杂,急弯、长下坡、桥隧密集,车辆通行的安全风险点多,管控难度大。长安大学“安行智构”团队研发国产化公路安全仿真平台,依托数字模拟技术提前识别道路隐患,为山区公路安全整治提供技术支撑。
中新网西安11月11日电 (记者 党田野 张一辰)山区公路地形复杂,急弯、长下坡、桥隧密集,车辆通行的安全风险点多、管控难度大。长安大学“安行智构”团队研发国产化公路安全仿真平台,依托数字模拟技术提前识别道路隐患,为山区公路安全整治提供技术支撑。近日,记者走进实验室探访这支科研团队。
图为长安大学“安行智构”团队负责人张驰正和工作人员分析道路风险数据。党田野 摄
在工作室内,团队负责人张驰正和工作人员一同梳理、分析道路风险数据。张驰表示,山区公路现场行车测试实施难度大,本身存在较高安全隐患,拿到工程方提供的道路基础资料后,科研人员即可搭建虚拟路况环境,开展多种行车工况下的仿真研判。
“借助这套技术,我们可以在设计阶段对路线、桥梁、隧道等危险路段做可视化呈现。初代产品已经研制完成,导入道路设计数据就能生成仿真场景,由经验丰富的驾驶员开展模拟驾驶,从中排查路段缺陷。”张驰称,将风险评估前移至设计环节,能够规避实地测试带来的安全风险。
据了解,模拟驾驶舱是实验的核心设备,能够还原车辆颠簸的真实驾乘感受,复刻山区道路行车场景,同步采集各类行车数据。
图为工作人员正在使用模拟驾驶舱。长安大学供图
“普通驾驶模拟器很难还原过减速带时液压杆的震动反馈,我们这套设备可以还原货车真实颠簸状态,模拟过程中同步采集卡车运行数据。”长安大学公路学院学生徐学理说,贴近真实的体感反馈和动态数据采集能力,是科研模拟器和娱乐类模拟器最核心的区别。
采集到完整行车数据后,工作人员随即开展风险研判。已经通车的山区公路,如果采用传统整改方案,往往需要封闭道路施工,直接影响沿线民众出行。借助仿真系统,工作人员不用大规模开挖重建,就能精准锁定制动风险点位,通过优化标志标线、客货分道、分级限速等轻量化措施补齐安全短板。
“雅西高速有一段51公里连续长下坡路段,此前重载货车事故多发,我们针对性实施交通特管区整改,合理布设停车区、降温池,优化避险车道位置,该路段现已实现年度零伤亡。”长安大学公路学院博士生韦涤斌称,这一落地案例,检验了仿真技术在山区公路隐患治理中的实用价值。
据介绍,公路安全仿真平台研发需要车辆、路面、信息可视化等多个方向的知识相互支撑,单一领域很难完成整套系统的迭代。
“仿真研究不只是复刻车辆行驶画面,还要厘清车辆运行、轮胎与路面交互的底层机理,依托信息技术完成场景可视化输出,才能适配更多复杂的公路与交通场景。”张驰称,随着多学科持续融合,这套技术也将不断完善,持续为山区公路安全治理探索新路径。(完)
AI outlook — possibilities, not facts
公路安全仿真平台将在更多山区公路项目中推广应用
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