GOALI: Modeling, Evaluation, and Control of Tire Blowout for Automated and Partially Automated Vehicles

这个赠款机会学术联络与工业（GOALI）项目推进有效的系统建模，预测和减轻轮胎爆裂对汽车动力学的影响的研究基础。该项目的目标是大幅改善道路安全，特别是随着驾驶员辅助系统或完全自动驾驶在个人车辆中越来越普遍。三个具体的研究重点得到解决。第一个研究重点是对爆胎事件本身进行深入的建模--本质上是对轮胎迅速失去完整性时发生的变化进行“超慢动作”观察。将该模型嵌入到汽车仿真软件中，预测各种车辆在不同环境条件下的动态响应。由于这种高保真的建模方法将井喷的短暂持续时间分割成许多微小的时间间隔，因此产生的计算机软件太慢，无法在实际事件中使用。因此，第二个研究重点是创建一个可以由车载计算机运行的爆胎模型，以帮助控制汽车。“脉冲”控制模型将井喷事件浓缩为车辆状态的单个跳跃变化。车载计算机使用脉冲模型来解释传感器读数，确定发生的井喷类型，并预测汽车随后的运动。最后，车载计算机使用模型来控制汽车，直到它可以安全停止。这种控制器适用于自动驾驶车辆，但在不久的将来，更有可能看到部分自主的部署，其中驾驶员辅助功能与人类操作员共享一定程度的控制。对于部分自主的情况下，车载控制器必须能够预测和适应操作员的输入。因此，该项目的第三个研究重点是创建与车辆控制系统集成的人类行为模型，这将实现有效的共享控制，并确保在各种驾驶员技能和情绪状态下的安全操作。该项目的成功实施，每年可避免数万起事故和数百人死亡。与通用汽车公司的研究伙伴关系将促进研究结果向商业实践的转化，通用汽车公司将提供主题专业知识，并在通用汽车公司的设施中进行全尺寸测试，以验证比例模型实验室实验。三项任务中的第一项将推动研究小组的初步努力，以建立经过实验验证的高保真轮胎爆裂模型。该模型的特点是在爆胎过程中的两个阶段的力量分析，与第一阶段占轮胎的初始崩溃，和第二阶段占车轮的行为后，轮辋接触地面。这种高保真模型将通过轮胎压力、轮胎尺寸、转弯刚度和车辆几何形状等参数进行参数化，并将包括车辆行为对这些参数的敏感性分析。大的时间尺度之间的分离的动态的轮胎爆裂和前面和后面的车辆动力学使得这个模型太笨重的实时使用。因此，第二个任务是推导并验证爆胎前后车辆动力学的脉冲模型。也就是说，爆胎事件本身的动态被忽略，并且其影响被聚集成施加在受撞击的轮胎处的瞬时冲击力，加上车辆参数中的相关联的阶跃变化。该模型的版本将获得两种类型的估计-的情况下，爆胎的身份是已知的，和的情况下，它必须推断。考虑完全自主和部分自主车辆都是有意义的。对于后者，人类驾驶员与自动控制器共享控制。在共享控制下，自动控制器应该能够解释和预测人类驾驶员的输入，以便提供适当的稳定响应。因此，第三个研究任务是创建可由车辆控制器实现的人类行为模型。所设想的模型表示的动态响应车道偏离误差使用三阶传递函数的时间延迟。随后，该项目将获得控制器，以确保自动化和部分自动化车辆在爆胎后的安全车道维护。在这项任务中的创新包括推导出动态模型相结合的脉冲和连续控制和干扰。该项目还将开发脉冲可观测性工具，以描述从传感器测量中推断井喷特征的条件。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

This Grant Opportunities for Academic Liaison with Industry (GOALI) project advances a research foundation for effective systems to model, predict, and mitigate the effects of tire blowout on automobile dynamics. The goal of this project is to substantially improve road safety, especially as driver-assist systems or full autonomy become more widespread in personal vehicles. Three specific research thrusts are addressed. The first research thrust is an in-depth model of the blowout event itself -- essentially a "super slow-motion" look at the changes that occur as the tire rapidly loses its integrity. This model is embedded in a vehicle simulation software, to predict the dynamic response of various vehicles, under different ambient conditions. Because this high-fidelity modeling approach splits the brief duration of the blowout into many tiny time intervals, the resulting computer software is too slow to be used during an actual event. Therefore, the second research thrust is to create a model of tire blowout that can be run by an on-board computer to help keep the car under control. An "impulsive" control model condenses the blowout event into a single jump change in the vehicle status. An on-board computer uses the impulsive model to interpret sensor readings, determine the type of blowout that has occurred, and predict the subsequent motion of the car. Finally, the on-board computer uses the model to keep the car under control until it can be brought to a safe stop. This controller is appropriate for self-driving vehicles, however the near future is more likely to see deployment of partial autonomy, in which a driver-assist feature shares some degree of control with a human operator. For partially autonomous situations the on-board controller must be able to predict and accommodate operator inputs. Therefore, the third research thrust of the project is the creation of a human-behavior model to be integrated with the vehicle control system, that will enable effective shared control and ensure safe operation over a wide range of driver skills and emotional states. Each year tens of thousands of accidents and hundreds of deaths could be prevented by a successful outcome to this project. The transition of the results to commercial practice will be facilitated by a research partnership with General Motors, who will provide subject-matter expertise, and validate scale-model laboratory experiments with full-scale testing at GM facilities.The first of three tasks will advance the research team's preliminary efforts towards an experimentally validated, high-fidelity model of tire blowout. The model features a two-phase analysis of forces during blowout, with the first phase accounting for the initial collapse of the tire, and the second phase accounting for the wheel behavior after the rim contacts the ground. This high-fidelity model will be parametrized by quantities such as tire pressure, tire size, cornering stiffness, and vehicle geometry, and will include an analysis of the sensitivity of vehicle behavior to those parameters. The large time-scale separation between the dynamics of the tire blowout and the preceding and subsequent vehicle dynamics makes this model too unwieldy for real-time use. Therefore, the second task is to derive and validate an impulsive model of the vehicle dynamics before and after tire blowout. That is, the dynamics of the blowout event itself are ignored, and its effects are gathered into an instantaneous impulsive force applied at the stricken tire, plus an associated step change in the vehicle parameters. Versions of the model will be obtained for two types of estimators -- the case where the identity of the blown-out tire is known, and the case for which it must be inferred. It is of interest to consider both fully autonomous and partially autonomous vehicles. For the latter, a human driver shares control with an automatic controller. Under shared control, the automatic controller should be able to interpret and predict the human driver input, in order to provide an appropriate stabilizing response. Thus the third research task is to create a model of human behavior, implementable by the vehicle controller. The envisioned model represents the dynamics of human response to lane departure error using a third-order transfer function with time delay. Subsequently, the project will derive controllers to ensure safe lane maintenance after tire blowout for both automated and partially automated vehicles. Innovations in this task include the derivation of dynamic models combining impulsive and continuous controls and disturbances. The project will also develop tools of impulsive observability to describe conditions under which the blowout characteristics may be inferred from sensor measurements.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.