Symbolic dynamics and model-based control of autonomous vehicules

自动驾驶技术将改变汽车行业，极大地提高车辆安全和运输效率，并为成千上万无法独自处理驾驶任务的人提供低成本的机动性。由于预期未来几年将取得重大进展，私营部门和公共部门的投资正在增加。谷歌雄心勃勃的计划承诺到2020年实现全自动车辆，但谷歌非常昂贵的最先进车辆在强制脱离无人驾驶系统之间只实现了2000公里。一项有前途的技术可以控制自动驾驶车辆，使它们避免静止物体和移动的汽车或行人，这被称为“模型预测控制”。本质上，汽车的动态模型预测未来的短时间；控制器使用这种预测来控制转向、刹车和油门。模型越好，控制就越好。然而，模型预测控制器必须在汽车的计算机上运行得非常快，特别是在需要紧急动作的时候。符号计算是由Maplesoft开创并商业化的一项技术。它允许直接处理模型和控制器中的数学方程。这些方程不仅可以象征性地简化，还可以用来生成极快的仿真代码--比如自动驾驶汽车所需的代码。这项研究的目标是开发新的理论和符号计算软件，自动生成非常快的模型预测控制器；目标应用是滑铁卢大学的AutonoMoose，这是第一款获准在安大略省道路上使用的自动驾驶汽车。提出的新理论和基于Maplesoft的算法将提高车辆控制器的性能，加速加拿大和国外自动驾驶汽车的发展。反过来，这将增加世界各地的汽车制造商和研究人员对Maplesoft软件的采用。

Autonomous driving technologies will transform the automotive sector, greatly increasing vehicle safety and transportation efficiency, and providing low-cost mobility to thousands unable to handle the task of driving on their own. Private and public sector investment is ramping up in the expectation of significant progress over the next few years. Google's ambitious program promises fully automated vehicles by 2020, but Google's very expensive state-of-the-art vehicles are achieving only 2000 km between forced disengagements of the driverless system.A promising technology to control autonomous vehicles so that they avoid static objects and moving cars or pedestrians is known as "model-predictive control". Essentially, a dynamic model of the car predicts a short time into the future; the controller uses this prediction to control the steering, brakes, and throttle. The better the model, the better the control. However, the model-predictive controller must run very quickly on a car's computers, especially when emergency maneuvers are required.Symbolic computing is a technology pioneered and commercialized by Maplesoft. It allows the direct manipulation of the mathematical equations within the model and controller. Not only can the equations can be simplified symbolically, they can be used to generate extremely fast simulation code - such as that needed by an autonomous vehicle.The goal of this research is to develop new theories and symbolic computing software that automatically generates very fast model-predictive controllers; the target application is the University of Waterloo's AutonoMoose, the first autonomous vehicle approved for Ontario's roads. The proposed new theories and Maplesoft-based algorithms will improve the performance of vehicle controllers, and accelerate the development of autonomous vehicles in Canada and abroad. In turn, this will increase the adoption of Maplesoft software by automotive manufacturers and researchers around the world.