Artificial intelligence in transport

Main article: Artificial intelligence (AI), Artificial intelligence (AI)

2025

BMW capitulates: The Chinese neural network DeepSeek is being introduced into the car

On April 22, 2025, it became known that BMW intends to use artificial intelligence technologies developed by the Chinese company DeepSeek in its cars. We are talking about vehicles for the PRC market. Read more here

Mosgortrans began to use AI to form driver schedules

In mid-April 2025, it became known that GUP Mosgortrans introduced an artificial intelligence system to form driver schedules at 19 production sites, which is about 75% of all enterprise bases. Thanks to the new technology, the process of selecting routes and shifts was reduced from two hours to five minutes. Read more here

Mosgortrans has introduced an AI system for automatic assignment of drivers to routes

Moscow City Transport (Mosgortrans) has introduced a new system for automatically assigning drivers to routes using artificial intelligence. Deputy Mayor of Moscow for Transport and Industry Maxim Liksutov in mid-February 2025 said that about 30% of drivers are already working under the new system. Read more here.

2024

The volume of the Russian market for AI technologies for the automotive industry for the year increased to 6.4 billion rubles

At the end of 2024, the costs of the Russian artificial intelligence market in the automotive industry reached 6.4 billion rubles. This sector is growing rapidly, as stated in a study by Trust Technologies, the results of which TAdviser got acquainted with in mid-June 2025.

The report notes that the main growth of AI in the Russian automotive industry is provided by autonomous systems, platforms for electric vehicles, computer vision and predictive analytics technologies, as well as modular production solutions. In service centers, AI is used to diagnose cars and predict possible breakdowns, which allows you to speed up repairs and improve their quality. In addition, AI assistants analyze customers' call history and preferences to offer personalized services and improve the quality of service. In manufacturing, AI is used to improve automation processes in order to increase production volumes.

One of the main drivers of the analytics industry is the development of commercial autonomous vehicles. We are talking primarily about automation of operations with simple navigation conditions - these are combines, trams, warehouse transport, main tractors, etc. The integration of cars with the infrastructure of smart cities has a positive impact on the market: in particular, V2X technologies are used, which serve to interact vehicles with other facilities. AI helps personalize the driver's experience, forming an individual profile, and also helps optimize energy consumption. In addition, various state projects in the field of AI are actively being implemented in Russia.

At the same time, certain restraining factors stand out. This, in particular, is the postponement of the introduction of autonomous systems in the Russian Federation, caused by the suspension of joint projects with Western investors and the lack of domestic analogues of key technologies. Strengthening regulatory requirements for AI increases the time and cost of development. There are difficulties with the transition to 5G technology, which is critical for the development of "smart mobility." The industry is negatively affected by restrictions on the import of key components necessary for AI (microprocessors, video cards, etc.).

In general, AI in the automotive industry in the Russian Federation is actively developing, despite a shortage of technologies and tightening regulatory requirements. In Russia, out of five key areas of "smart mobility," AI is being introduced into "Connected Car," "Telematics Transport " and "Autonomous Driving." Two more areas - Mobility as a Service (MaaS) and Electrification - are poorly covered by AI.

The authors of the study point to targeted state support and regulatory initiatives that are designed to accelerate the development of unmanned vehicles in Russia. Thus, the federal project "Artificial Intelligence" has been added to the national program "Digital Economy." And within the framework of the Unmanned Logistics Corridors project, as of the date of the report's formation, 7.2 million km were traveled by unmanned tractors and 800 thousand cubic meters of cargo were transported. The use of such machines increased the speed of transportation by 11%, while the cost of fuel and components decreased to 14%.

In the future, it is expected that the volume of the Russian AI technology market for the automotive industry will continue to develop rapidly. In 2025, costs are projected at 23.7 billion rubles, which will correspond to an increase of about 270% compared to the previous year.^[1]

Artificial intelligence could make rail companies billions of dollars a year

An international specialist IT in the field of railway transport told how artificial intelligence he can increase safety, level of service and demand while minimizing costs. More. here

2020: FEFU and MIPT develop mathematical algorithms for solving transport problems and working with data

On April 28, 2020, it became known that scientists from the Far Eastern Federal University (FEFU), together with colleagues from the Moscow Institute of Physics and Technology (MIPT), are developing mathematical methods of convex optimization to accelerate the solution of the widest range of problems in economics, science, and many applied areas of human activity. Scientists reported their results in the book "Numerical Methods of Convex Optimization" by Springer.

According to the company, algorithms they are adaptive, that is, in the process of work distinguish , all the necessary parameters themselves are economical, and their work requires a relatively small amount of memory. It is advisable to use these algorithms, for example, to simulate transport flows, combat traffic jams and optimize freight routes, transport calculate fares, rank web pages, solve reverse problems when it is necessary to understand the reasons that gave rise to some consequences.

Non-smooth or convex optimization is based on the decomposition principle. This means that a large task can often be divided into many small ones, which are then linked to each other using a special coordinating task. For April 2020, this is relevant for working with big data. In the modern world, there is often a need to process, transmit data measured by gigabytes or more, as well as solve very complex problems on their basis. A naive direct approach, even using the fastest supercomputers, will take hundreds and thousands of years to solve such problems. Mathematics accelerates these processes so much that they acquire practical meaning. told Evgeny Nurminsky, Professor of the School of Natural Sciences of FEFU

The scientist said that for the classical problem of solving a system of linear equations, modern algorithms are many times more efficient than traditional methods, the labor intensity of which is approximately equal to the cube of the number of variables.

If there are 5 variables in the task, then you spend 125 operations and, say, 1 second of time to solve it. If there are 50 variables, then you will need 125 thousand operations and about 15 minutes. Imagine that the variables are 5000. It will take about 30 years to solve the problem in the traditional way. These methods will reduce this time to 40 seconds. Of course, you can spend tens of billions of rubles. or dollars, build a supercomputer the size of the Cheops pyramid and the power consumption of an icebreaker, which will still solve your problem in a day. But is it not better to allocate a thousandth of this amount for talented students who will do much more? Of course, the supercomputer will not hurt! supplemented by Evgeny Nurminsky, professor at the School of Natural Sciences of FEFU

Based on algorithms, you can create a way to process a "heavy" image so that at the output it requires 10 times less space than at the input, but retains 95 percent of the original properties. At the same time, such a picture cannot be distinguished by eye from the original one.

Speaking very mundane, optimization helps to dig less both literally and figuratively. For example, you need to decide how to dig an underpass connecting four points at an intersection so that you can get into any exit on the other side of the road from any entrance. It would seem that you need to draw a square and dig tunnels along its two diagonals. Mathematics tells us that for less labor, digging will have to be different. By designing very large mechanical structures, convex optimization methods can calculate how to obtain the smallest mass of these structures without losing strength. Another example is that convex optimization helps determine the optimal way to collect tolls on toll roads, leading to minimization of the total losses of network users on the road. explained Alexander Gasnikov, Associate Professor, Department of Mathematical Foundations of Management, Moscow Institute of Physics and Technology

The scientist noted that optimization problems are directly related to life, that is, nature itself often speaks the language of mathematics, and in order to understand its structure, it is necessary to solve the optimization problem.

In complex (non-convex) problems, however, in most cases we cannot get an ideal solution, but often this is not required. In practice, suboptimal results obtained with some error, but in a reasonable time, are often quite satisfied. This applies, for example, to many deep learning tasks. added Alexander Gasnikov, Associate Professor, Department of Mathematical Foundations of MIPT Management

The book "Numerical Methods of Convex Optimization" reveals traditional and more modern methods of convex optimization. The work is intended for students, faculty, academics and practitioners whose field of activity involves convex optimization. Scientists of FEFU and MIPT spoke about their methods of convex optimization in a separate chapter "Subgradient methods for solving the problem of convex optimization with low memory costs."

Publishers have assembled under one cover leading scientists who are developing the field of convex optimization. The book can be useful for anyone who wishes to obtain up-to-date information about the state of affairs and the tools that this field of mathematics contains.

Convex optimization algorithms can be applied to the correct construction of real world models, and in areas such as data collection, processing and transmission machine learning , and, artificial intelligence engineering, sciences economics and business, computer chemistry, physics and. medicine