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in the field of intelligent transportation, vehicle-road-cloud integration is becoming a hot topic. in a recent interview with seven transportation network's "vehicle-road-cloud integration: finding certainty" column, guo xingrong, cto of mushroom car network, expressed an important perspective for us to deeply understand the trend of vehicle-road-cloud integration.

1. vehicle-road collaboration has obvious advantages

guo xingrong pointed out that through comparative tests, it was found that vehicle-road collaboration performed better than single-vehicle intelligence in terms of safety takeover times, comfort and energy consumption. under the same road section and driving conditions, the number of takeovers with the addition of roadside data was reduced by more than 20%, riding comfort was improved by 10%, and power consumption was also reduced. this means that vehicle-road collaboration can not only improve travel safety, but also bring a more comfortable experience to passengers, while reducing energy consumption, which meets the requirements of sustainable development.

2. technological breakthroughs are imminent

however, vehicle-road collaboration also faces some technical challenges that need to be overcome urgently.

data governance: if the data of roadside infrastructure is to be used by vehicles, the perception and computing accuracy must be high. in the past, some places only focused on equipment installation during construction, but ignored the quality and application of data, resulting in data vehicles not receiving data or receiving inaccurate data. this not only affects the effect of vehicle-road collaboration, but also makes people question its reliability. therefore, strengthening data governance and improving data quality are the key to the development of vehicle-road collaboration.

latency issue: latency is crucial for vehicle-road collaboration. when driving at high speeds, a millisecond-level delay can lead to serious consequences. therefore, reducing latency and improving the speed and accuracy of data transmission are issues that must be addressed in vehicle-road collaboration.

3. interface standards: at present, the interface standards of various places are not unified, which brings great difficulties to the demonstration operation of vehicle-road collaboration. vehicles have to re-connect interfaces in every place, which not only wastes time and resources, but also affects the promotion and application of vehicle-road collaboration. therefore, unifying interface standards and establishing industry norms are inevitable requirements for the large-scale development of vehicle-road collaboration.

3. vehicle-road collaboration improves safety and traffic efficiency

improve safety: vehicle-road collaboration buys time to improve the safety of autonomous driving. at present, the detection distance of a single vehicle is limited and is greatly affected by weather, night and other conditions. however, vehicle-road collaboration technology allows the car to see farther and send data beyond the visual range to the vehicle, providing the vehicle with more processing time. this is undoubtedly a huge advantage and can greatly improve the safety of autonomous driving.

improve traffic efficiency: single-vehicle intelligence is more about individuals seeking the optimal solution, but it may not be the best solution for the entire traffic system. the integration of vehicle, road and cloud can provide decision-making suggestions and planning for vehicles, thus improving traffic efficiency overall. through experiments, it was found that the traffic efficiency of single-vehicle intelligence is lower than that of human driving, but after adding cloud-based decision-making assistance, the traffic efficiency is stable with little fluctuation.

4. roadside data helps the development of autonomous driving

for autonomous driving, data is the key. at present, many companies rely on test cars to collect data on the road, which is costly and inefficient. tesla's autonomous driving fleet is the largest in the world, and chinese companies face great pressure in data collection. however, through the integration of vehicle, road and cloud, base stations or perception computing systems are set up on the roadside, and traffic conditions can be collected 24 hours a day, 7 days a week, providing a good data source for autonomous driving ai model training. this can not only reduce the cost of data collection, but also improve the quality and quantity of data, helping chinese companies catch up with international technology companies.

with the continuous advancement of science and technology, intelligent transportation has become an inevitable trend in the development of future transportation. as an important part of intelligent transportation, vehicle-road-cloud integration has received more and more attention and support. industry experts believe that vehicle-road-cloud integration can achieve the collaborative work of vehicles, roads and the cloud, improve traffic efficiency, reduce the incidence of traffic accidents, and provide people with a safer, more convenient and more efficient way of travel. at the same time, the government is also actively promoting the development of vehicle-road-cloud integration. various places have accelerated the construction of pilot applications, increased investment, and encouraged enterprises to carry out research and development and application of vehicle-road collaborative technology. this provides a good policy environment and market opportunities for the development of vehicle-road-cloud integration.