How to accelerate the commercialization of high-level autonomous driving applications

5. Suggestions on promoting the commercial application of autonomous driving

The expansion of autonomous driving commercial applications should be subject to urban development strategies

The essence of autonomous driving is to effectively improve driving safety and transportation efficiency. The application scenarios of autonomous driving must be placed in the context of coordinated development of transportation and urban space, and must comply with the overall urban development strategy. Therefore, it is recommended to expand the application of autonomous driving from the transportation demand side, mainly to meet the personalized needs of large passenger flows, with the goal of improving accessibility and mobility, and to make reasonable connections between different modes of transportation.

Specifically, a "one-stop" ticketing system window can be set up through roadside electronic bus stops, and combined with transfer facilities that can be quickly deployed, micro-circulation buses or public buses can be deployed around the connection of rail stations.

At the same time, it is recommended that when expanding applications on the transportation supply side, attention should be paid to the allocation of non-road urban infrastructure, and through the reorganization of its structure and distribution, people's travel choices can be guided, thereby improving the efficiency of the use of various resources and travel efficiency. Specifically, it can be combined with urban space transformation, and parking spaces and charging facilities can be allocated in hot spots to provide time-sharing rental services for new energy vehicles with autonomous driving functions.

Promoting the application of mature advanced driver assistance

The autonomous driving system in the urban development space itself has a huge technical system. It is relatively easy to optimize a certain functional module, achieve technological breakthroughs, and form a commercialization path.

Therefore, it is recommended that on the one hand, new assisted driving functions and connotations should continue to be gradually expanded, such as HWP (highway automatic driving), TJP (traffic congestion automatic driving), AVP (autonomous driving parking) and other functions; on the other hand, the mature advanced assisted driving functions at the L1-L4 levels should be continuously strengthened and improved, and the scenarios of integration with other traffic subsystems should be expanded.

For example, the TSR (traffic sign recognition) system is integrated with the traffic signal system, and through the Internet of Things devices, vehicles on the move can be informed of the intersection signal status and waiting time in advance.

Strengthen the construction of digital and intelligent foundations and plan the deployment of smart infrastructure

As a typical application that integrates comprehensive perception, mobile Internet, cloud computing, and artificial intelligence, autonomous driving cannot be separated from the digital city. Whether it is the decision-making and planning based on the perception of the location and surrounding environment, or the driving plan executed by the roadside unit to obtain the road conditions, it is inseparable from the support of data and computing power.

Therefore, it is recommended to study the equipment standards and reasonable layout of smart infrastructure at different nodes and sections in combination with application scenarios, aiming at the pain points of commercial operation of autonomous driving at this stage, to avoid problems such as over-construction and under-construction. On the basis of enriching traffic data resources, further guide the rational distribution of factors affecting traffic travel demand. At the same time, on the basis of cloud computing centers, application computing centers such as large-scale capacity dispatching platforms and travel reservation service platforms are built.

At the same time, efforts should be made to establish a data market with unified specifications, standards and values, break the shackles of information barriers on digital intelligence, give full play to the role of large artificial intelligence models, coordinate the operation between systems, and consolidate the "digital foundation" of urban transportation required for high-level applications of autonomous driving. In terms of test scenario expansion, it is recommended to learn from Tesla's scenario data construction practices to expand the data set.

Carry out research on traffic reconstruction issues caused by autonomous driving as soon as possible

At present, autonomous driving is not yet at the stage where it can completely replace drivers, and the overall impact of autonomous driving cities on urban travel demand is not clear. However, as multiple factors such as technology maturity, travel costs, policy support, and road facilities change, it is bound to lead to the reconstruction of future transportation, and relevant research should be carried out as soon as possible.

The research should start from the aspects of stabilizing traffic safety operation, avoiding potential application risks, protecting the legitimate rights and interests of drivers and passengers, and stable and efficient commercial application, and carry out special research on implementation guarantee conditions and corresponding risk assessment. The research directions include but are not limited to laws and regulations, transportation infrastructure planning and design, transportation organization changes (such as appointment travel transportation organization mode), transportation operation management, etc.