The main goal of a new study by J'son & Partners Consulting was to analyze the experience of creating civil aviation safety systems and the possibility of using such organizational approaches to improve the safety of motor transport carrying dangerous cargos, primarily fuel and lubricants. The authors analyzed the level of technical implementation of the current and future technologies that allow increasing the level of active safety of trucks, as well as the possibilities to automate driving functions ("digital assistants", ADAS of various levels).

 

Improving the safety of oil products transportation by motor transport remains an urgent task. In contrast to the passenger car segment, in Russia, the number of accidents involving trucks and the number of fatalities in such accidents does not decrease. The problem of freight transport safety is also relevant for developed countries.

 

In order to reduce the accident rate of cargo transportation and its harmful impact on the environment increasing the cargo safety performance at the same time, manufacturers gradually automate trucks using advanced driver-assistance systems (ADAS) and develop fully autonomous trucks that do not require any actions from a driver. The implementation of unmanned vehicles becomes impossible without creating smart road infrastructure which is also being developed now. This includes intelligent transport systems (ITS) and vehicle communication systems that allow connecting a car with other cars, infrastructure, pedestrians, and other road users (V2X).

 

In the transport industry, particularly in trucking, we can see a trend to actively introduce new technologies, first of all, external and internal vehicle communication systems. The endpoint of these emerging trends is to connect existing fleets to the global network, as well as to create a mechanism for unmanned trucking. Companies such as Daimler Trucks, Volvo, Paccar, MAN, Scania, and other brands are the market leaders both in terms of revenue and innovations.

 

Russia is characterized by an old, worn-out fleet of trucks, three-quarters of the national truck fleet are domestic vehicles, which practically do not use innovative solutions which could improve safety and increase the level of automation of driver actions. At the same time, the national truck fleet has been actively updated in recent years.

 

Russian car manufacturers, industrial and IT companies, system integrators, and other players are developing ADAS systems with the prospect of creating a driverless car, but there are no commercial implementations of such systems yet. As a rule, only large transport companies that have a large (hundreds of units) fleet of modern major foreign brand trucks can afford to use innovative solutions.

 

Global trends in trucking are autonomous driving, vehicle electrification, and connectivity of trucks to single information space. Russia urgently needs smart roads, 5G Internet, charging infrastructure for electric vehicles, as well as a legislative program to toughen automobile technical regulations, concerning the introduction of vehicle safety requirements such as active and passive driver-assistance systems (ADAS).

 

On November 1, 2013, the EU countries introduced the requirement to equip all new models of trucks weighing 3.5 tons or more with AEBS and LDWS systems. In addition, for some truck categories, the ESC system is mandatory from November 1, 2011.

 

In general, trucking automation should be considered in a broader aspect — not only in terms of improving safety (reducing the number of accidents and victims and improving the safety of cargo) but also as an effective way to:

 

- save fuel and reduce harmful emissions;

 

- simplify the car control process;

 

- improve operational efficiency (by planning in real-time and reducing truck downtime);

 

- reduce labor costs (transport automation gradually reduces the need for human drivers).

 

Section 1 of this study analyzes ways of detecting traffic safety threats basing on air transportation. In this section, the authors assess the applicability of risk management approaches and methodology used in aviation for trucking.

 

Section 2 analyzes the experience of implementing digital assistance projects on trucks in major international and Russian transport companies. As the analysis of accidents involving heavy trucks shows, the introduction of driver-assistance systems ADAS (the first level of vehicle automation) significantly reduces accidents.

 

Section 3 analyzes the limit of automation of driver actions taking into account the development of technologies. The study gives examples of providing trucks with various automation systems. Heavy truck automation is considered in the context of the main components and technologies used, with a brief description, status, and examples of supplier companies. This section provides an analysis of the ADAS functionality, as well as the cost of implementing technologies and solutions for automation and the connection of heavy trucks.

 

Section 4 analyzes the existing systems for monitoring and communication of external objects, systems, and services (V2X) and gives prospects for their development. The authors performed an assessment of the applicability of V2X technologies to increase the level of automation in trucking and an analysis of costs for a fleet of 1000 vehicles.

 

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This information note was prepared by the J'son & Partners Consulting. We work hard to provide factual and prognostic data that fully reflect the situation and available at the time of release. J'son & Partners Consulting reserves the right to revise the data after publication of new official information by individual players. 

 

Copyright © 2020, J'son & Partners Consulting. The media can use the text, graphics, and data contained in this market review only using a link to the source of information - J'son & Partners Consulting or with an active link to the JSON.TV portal

 

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Detailed research results are presented in the full version of the report: 

“Digitalization in freight transportation. Driver-assistance and vehicle safety systems (ADAS, ITS, V2C/V2X, etc.)”

Contents

SUMMARY

TERMS, ABBREVIATIONS AND DEFINITIONS          

INTRODUCTION

1. ANALYSIS OF MEANS FOR DETECTING TRAFFIC SAFETY THREATS BASED ON AIR TRANSPORTATION

1.1. DETERMINING THE LIST OF RISK FACTORS

1.2. SYSTEMS FOR IDENTIFYING HAZARDS AND RISK FACTORS

1.3. METHODS OF ANALYSIS AND ASSESSMENT OF THE DEGREE OF HAZARD OF THE IDENTIFIED RISK FACTORS

1.4. SYSTEMS AND METHODS FOR DEVELOPING OPTIONS FOR LOCALIZING RISK FACTORS

1.5. PILOT INFORMATION SYSTEMS

1.6. SYSTEMS FOR ANALYZING MEASURES TAKEN

1.7. ASSESSMENT OF THE APPLICABILITY OF APPROACHES AND METHODOLOGY OF RISK MANAGEMENT IN AVIATION FOR ROAD TRANSPORTATION

2.  ANALYSIS OF EXPERIENCE OF IMPLEMENTING DIGITAL ASSISTANT PROJECTS ON TRUCKS

2.1. ANALYSIS OF PROJECT IMPLEMENTATION EXPERIENCE IN RUSSIAN COMPANIES

2.1.1. "Magnit"

2.1.2. "Delovye Linii"

2.1.3. ZhelDorExpeditsiya

2.1.4. Delko

2.1.5. "Lider Trans"

2.1.6. Traft

2.2. ANALYSIS OF PROJECT IMPLEMENTATION EXPERIENCE IN FOREIGN COMPANIES

2.2.1. Mobileye ADAS solutions

2.2.2. Use of tablet PCs

2.3. CONCLUSIONS AND RECOMMENDATIONS

3.         ANALYSIS OF THE DRIVER AUTOMATION LIMIT TAKING INTO ACCOUNT THE TECHNOLOGY DEVELOPMENT

3.1. AUTOMATED DRIVING SYSTEMS 

3.1.1. Cars

3.1.2. Trucks

3.1.3. Trends in the automated driving system market

3.1.4. Key global and Russian players

3.1.4.1. Automakers

3.1.4.1. Manufacturers and developers of automotive components

3.1.4.2. Manufacturers of chipsets and hardware platforms

3.1.4.3. IT companies

3.1.4.1. Developers of telematics systems

3.1.4.2. Startups

3.1.4.3. Suppliers of driver health monitoring systems

3.2. ANALYSIS OF THE AUTOMATION DEVELOPMENT VECTOR FOR 5 AND 10 YEARS

3.2.1. In the world

3.2.2. In Russia

3.2.2.1. "Caravan" Project

3.3. MAIN BARRIERS TO DEVELOPMENT

3.3.1. User ignorance

3.3.2. High cost of repairing vehicles equipped with ADAS

3.3.3. Other constraints

3.4. CONCLUSIONS AND RECOMMENDATIONS

4.         ANALYSIS OF SYSTEMS FOR MONITORING AND COMMUNICATION WITH EXTERNAL OBJECTS, SYSTEMS AND SERVICES

4.1. ANALYSIS OF MODERN TRUCK SELF-DIAGNOSTICS SYSTEMS 

4.1.1. Market analysis

4.1.2. Development trends

4.1.3. Major global and Russian players

4.1.3.1. Volvo Trucks

4.1.3.2. Scania Remote Diagnostics

4.1.3.3. Kenworth TruckTech+

4.1.3.4. TECH (E TRUCK)

4.1.3.5. Geotab

4.1.3.1. Smart Driving Labs

4.1.3.2. Other solutions

4.2. ANALYSIS OF SYSTEMS FOR COMMUNICATION AND MONITORING OF EXTERNAL FACTORS

4.2.1. Analysis and application of the Vehicle-to-Vehicle technology

4.2.1.1. General description of the technology

4.2.1.2. Market research

4.2.1.3. Development trends

4.2.1.4. Major global and Russian players

4.2.2. Analysis and application of the Vehicle-to-Cloud technology

4.2.2.1. General description of the technology

4.2.2.2. Market research

4.2.2.3. Development trends

4.2.2.4. Major global and Russian players

4.2.3. Analysis and application of Vehicle-to-Infrastructure technology

4.2.3.1. General description of the technology

4.2.3.2. Market research

4.2.3.3. Development trends

4.2.3.4. Major global and Russian players

4.2.4. Assessment of the technology implementation in Russia for the next 5 years

4.3. ASSESSMENT OF THE APPLICABILITY OF V2V, V2I, V2X TECHNOLOGIES FOR INCREASING THE LEVEL OF CARGO TRANSPORTATION AUTOMATION

4.3.1. Technical assessment

4.3.1.1. V2V Communications

4.3.1.2. V2I Communications

4.3.1.3. V2X Communications

4.3.2. Financial assessment for a fleet of 1000 vehicles

4.4. CONCLUSIONS AND RECOMMENDATIONS

5. ANNEXES

5.1. TRUCK FLEET IN RUSSIA AND NEW TRUCK SALES

5.2. CLASSIFICATION (TYPES) OF RADARS AND LIDARS, TRENDS AND FORECASTS

5.2.1. Radars

5.2.2. Lidars

 

List of figures

Fig. 1. Flight safety management evolution

Fig. 2. Accident causality concept          

Fig. 3. Functional diagram of an aviation security management system, definitions of acceptable risk

Fig. 4. K (100000) showing the dependence of the number of accidents in the total flying time for Russian civilian airplanes

Fig. 5. PCP showing the dependence of the number of accidents in the total flying time for Russian civilian helicopters

Fig. 6. Example of probabilistic-statistical models of aviation event development

Fig. 7. Example of division of aviation events by factor

Fig. 8. Numbers of operations performed by a pilot in different stages of the flight

Fig. 9. Correlation of the pilot's age with the number of accidents

Fig. 10. Aircraft cockpit avionics architecture evolution

Fig. 11. The overall layout of the cockpit environment 

Fig. 12. General view of an Airbus A320 dashboard

Fig. 13. Airbus A320 captain’s dashboard

Fig. 14. Airbus A320 central instrument panel

Fig. 15. Safety management process

Fig. 16. Shares of transport companies in the Russian market for long-distance trucking of consolidated cargo*: TOP 10, 2016

Fig. 17. Effect of the ADAS systems implementation on trucks in the USA

Fig. 18. Basic functionality of modern ADAS systems

Fig. 19. Driver-assistance systems in new passenger cars in Germany*

Fig. 20. Share of new vehicles equipped with ADAS systems (park assist, automatic emergency braking, lane guard system) in Europe and Russia

Fig. 21. Main limitations of adaptive cruise control*

Fig. 22. DENSE system architecture with a set of sensors for work in difficult weather conditions in 24/7 mode

Fig. 23. Capabilities of ARNI, an artificial intelligence-based smart system for information search used by transport companies and truck drivers

Fig. 24. Forecast of the global market for semiconductors used in ADAS systems, 2015-2025

Fig. 25. Top companies by number of patents in the field of autonomous driving*,

Fig. 26. The sequence of operation of the AEBS system in Volvo Trucks

Fig. 27. The appearance of the automatic transmission for Volvo Trucks

Fig. 28. Platooning of Volvo trucks

Fig. 29. Prototype of the autonomous tractor Vera for Volvo Trucks

Fig. 30. Driver-assistance and safety systems for Scania trucks

Fig. 31. Tablet PC with a specialized application for training Scania truck drivers

Fig. 32. Results of comparative tests of ADAS systems of the 2nd level

Fig. 33. World's biggest automotive lidars manufacturers

Fig. 34. ZF Innovation Truck with the automatic trailer connection/disconnection function

Fig. 35. ProAI System (left) and ReAX electro-hydraulic steering system (right)

Fig. 36: AC 1000 T Radar (left) and TriCam4 three-lens camera (right)

Fig. 37. Lidar produced by ZF together with Ibeo Auto

Fig. 38. 3D Flash Lidar from Continental

Fig. 39. Equipment used on the StarLine driverless car

Fig. 40. Comparison of various hardware platforms for autonomous cars

Fig. 41. The appearance of Mobileye devices: Series 5 and 6

Fig. 42. The appearance of the Mobileye Shield camera +

Fig. 43. Detection zones of the Mobileye Shield + system

Fig. 44. Zonar Coach ADAS system with accelerometer and video recorder

Fig. 45. Starsky Robotics remote driver operator workplace 

Fig. 46. Comparison of two driving patterns — in a cheerful (left) and tired (right) state

Fig. 47. Driver head and eye movement tracking device

Fig. 48. Electroencephalography device

Fig. 49. Bracelet for measuring the electrical conductivity of the skin

Fig. 50. Driver drowsiness detector SleepAlert 

Fig. 51. Penetration of ADAS vehicles (by level of automation) and robomobiles in the world, new passenger cars, 2014-2050

Fig. 52. ITS evolution: vehicle + road + network, 1997-2025

Fig. 53. Key stages on the way to autonomous driving, 2019-2027

Fig. 54. Technological road map for autonomous trucks,

Fig. 55. Key stages on the way to autonomous driving, 2019-2027

Fig. 56. Operating costs of truck fleet owners, 2016-2025

Fig. 57. Best-prepared countries for the introduction of autonomous vehicles*

Fig. 58. Awareness and consumer behavior in relation to ADAS systems in developed countries*

Fig. 59. Main concerns of potential consumers of autonomous vehicles in a number of countries*

Fig. 60. General scheme of predictive vehicle diagnostics

Fig. 61. Major participants of the market for predictive systems installed in commercial vehicles in North America and Europe

Fig. 62. Key truck parts covered by predictive systems

Fig. 63. Forecast of the market for predictive systems in North America and Europe, 2012-2020

Fig. 64. Which of the following functions best describe your choice of a telematics system?

Fig. 65. Typical interaction diagram when using the Kenworth TruckTech+ remote diagnostics service

Fig. 66. Types of v2x communications

Fig. 67. Communication technologies for the transport industry - Vehicular ad hoc networks (VANETs)

Fig. 68. DSRC Technology: key advantages and disadvantages, support at the level of auto manufacturers/vendors/industry consortia, and key events

Fig. 69. C-V2X Technology: key advantages and disadvantages, support at the level of auto manufacturers/vendors/industry consortia, and key events

Fig. 70. Comparison of DSRC and C-V2X technologies

Fig. 71. Main participants in the autonomous vehicle market associated with V2X

Fig. 72. Control devise with v2x technology from Bosch

Fig. 73. Typical scheme for organizing fleet management services

Fig. 74. StealsRey 7000 SOTM Satellite dish with automatic guidance

Fig. 75. Thuraya IP Voyager Satellite system: a modem and antenna

Fig. 76. Hybrid mobile phone Thuraya XT-PRO DUAL with satellite and cellular support

Fig. 77. Iridium GO! satellite modem

Fig. 78. Use of DSRC technology for contactless payment for toll roads in Russia

Fig. 79. Typical scenarios for using an RSU (Huawei) device with C-V2X support

Fig. 80. Main scenarios for using the C-V2X technology

Fig. 81. A possible implementation of V2X communications using DSRC and C-V2X technologies

Fig. 82. The simplified technical scheme of organizing an automated road train (platooning) at various levels of automation

Fig. 83. Additional costs for providing level 4 automation of a single truck (autopilot on a highway without a driver)

Fig. 84. Main types of car radars and their limitations

Fig. 85. Main characteristics of lidars on the market

Fig. 86. Global automotive lidar market, $ million, 2016-2032

 

List of tables

Table 1. Examples of accidents involving fuel trucks in Russia in the first decade of October 2018

Table 2. Probabilities of safety risk factors

Table 3. The severity of the risk factors for flight safety

Table 4. Safety risk assessment for flight safety

Table 5. A version of a safety risk assessment matrix

Table 6. Option for assessing the severity of safety risks

Table 7. Scheme of the state safety data system

Table 8. Types of analyzed accidents

Table 9. Assessment of the applicability of aviation SMS blocks in road cargo transportation

Table 10. Results of a survey of owners of heavy-duty truck fleets (class 8) in the United States: “are any of the ADAS systems installed on your vehicles?”

Table 11. Effect of using Mobileye ADAS systems

Table 12. Various levels of vehicle automation*

Table 13. Automation of heavy vehicles: key components and technologies, description, status, vendors

Table 14. Application of basic technologies for automation and connection of heavy trucks in advanced driver-assistance systems (ADAS)

Table 15. Functionality and limitations of ADAS devices

Table 16. ADAS functionality in Mercedes-Benz Actros trucks presented on the Russian market

Table 17. Timeline of driver-assistance/safety systems for Mercedes-Benz trucks, driverless vehicle developments

Table 18. Digital assistants installed on serial Volvo Trucks

Table 19. The functionality of ADAS systems in Paccar vehicles

Table 20. Evolution of Mobileye EyeQ chip systems

Table 21. Main functions and features of the Mobileye solution for the secondary market

Table 22. Key events of Mobileye N. V

Table 23. Examples of implementing satellite communication systems on vehicles: solution type, cost, data transfer speed, available services*

Table 24. Costs for implementing individual technologies and solutions for automation and connection of heavy trucks

Table 25. Costs for implementing technologies and solutions for automation and connection of heavy trucks (a fleet of 1000 vehicles)

Table 26. Russian truck fleet. Top 20 brands (as of 31.12.2017)

Table 27. Top 10 leaders of the Russian new truck market (units)

Table 28. Top 10 models of the Russian new truck market (units)