Читать книгу Engineering Autonomous Vehicles and Robots - Shaoshan Liu - Страница 14

Many major autonomous driving companies, such as Waymo, Baidu, and Uber, and several others are engaged in a competition to design and deploy the ultimate ubiquitous autonomous vehicle which can operate reliably and affordably, even in the most extreme environments. Yet, we have just seen that the cost for all sensors could be over $100 000, with the cost for the computing system another $30 000, resulting in an extremely high cost for each vehicle: a demo autonomous vehicle can easily cost over $800 000 [10]. Further, beyond the unit cost, it is still unclear how the operational costs for HD map creation and maintenance will be covered.

In addition, even with the most advanced sensors, having autonomous vehicles coexist with human-driven vehicles in complex traffic conditions remains a dicey proposition. As a result, unless we can significantly drop the costs of sensors, computing systems, and HD maps, as well as dramatically improve localization, perception, and decision-making algorithms in the next few years, autonomous driving will not be universally adopted.

Addressing these problems, a reliable autonomous vehicle has been developed by us and for low-speed scenarios, such as university campuses, industrial parks, and areas with limited traffic [11,12]. This approach starts with low speed to ensure safety, thus allowing immediate deployment. Then, with technology improvements and with the benefit of accumulated experience, high-speed scenarios will be envisioned, ultimately having the vehicle's performance equal that of a human driver in any driving scenario. The keys to enable affordability and reliability include using sensor fusion, modular design, and high-precision visual maps (HPVMs).