At 12.8% CAGR, Global Autonomous Truck Market Size to Surpass USD 2.14 Billion By 2027 | Industry Share, Trends & Forecast
The global Autonomous Truck Market size is expected to reach USD 2.14 Billion by 2027 according to a new study by Polaris Market Research. The report “Autonomous Truck Market Share, Size, Trends, Industry Analysis Report By Level of Autonomy (Level1, Level2, Level3, Level4, Level5); By Sensor Type (Radar, LiDAR, Camera, Ultrasonic); By End-Use (Logistics, Construction & Manufacturing, Mining, Port); By Region – Segment Forecast, 2020 – 2027” gives a detailed insight into current market dynamics and provides analysis on future market growth.
A rapid growth has been seen in the shipment of autonomous vehicles in the recent past, which has significantly encouraged truck manufacturers or new entrants in the market to invest in this novel technology. An increase in labor costs will create possibilities for self-driving Truck that use methods that reduce errors and risks with minimized human input, especially in emerging markets. Moreover, shifting production capacities to developing countries and improving economic conditions would result in the growth of the Autonomous Truck market. Consequently, new entrants will have possibilities in the industry. This is expected to be a major driving factor in the global self-driving trucks market.
The self-driving truck works on electricity; therefore, they are considered far more environment friendly, as compared to standard trucks. The electricity-based vehicle consumes less oil and gas and reduces fuel emissions, which in turn, contributes to the growth of the global market. However, there are certain factors that impede the market growth such of risk from hackers and increase cybercrime has limit the adoption of Autonomous Truck among consumers.
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Artificial Intelligence comprises many applications in the automotive sector such as in autonomous driving and Advanced Driver Assistance Systems (ADAS). The replacements of sensors, networks are widely used that facilitate the services of lane detection. Furthermore, artificial intelligence enables to collect a large amount of data for mobile devices and IoT sensors to improve the storage process and data collection. Though in China, the rate of adoption of AI has propelled in recent years. Moreover, with the on-going trends, it is expected that more and more automotive manufacturers will enter the market in the near future.
In autonomous vehicles, the automotive manufacturers and their technology partners are engaged in the development of AI-driven systems to drive the growth of the market. These systems integrate a broad range of AI-enabled technologies, including NLP and neural networks. These technologies offer several features that increase the understanding of vehicles, enabling the safe drive with or without driver on board. With the ever-increasing demand in urban mobility and modern logistics sector, the vehicle population has been steadily growing over the past few decades. The leading automobile giants are coming up with newer self-driving features in their latest models. The respective technological developments are not confined to just communication, information technology, and data analysis; the realm of self-driving cars is also making progress at a brisk speed nowadays.
The adoption of self-driving trucks has changed travel experience due to several advantageous features, such as minimum prices, high-speed, environmentally friendly nature and energy efficiency. Driving software glitches, however, are likely to hinder the growth of the market, as this software is prone to hackers. In addition, even though there’s an autonomous truck supervisor inside the vehicle, hackers can enter the software and override the controls. Thus, the growing threat from hackers and increased cybercrime are expected to limit autonomous truck industry developments in the coming years.
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A variety of techniques in Autonomous Truck are being implemented in the market that promotes environmentally friendly service, due to increasing environmental degradation and natural resource depletion. In addition, increasing sensitivity to air pollution effects and increased congestion in traffic drive the adoption of self-driven trucks, as they are fuel effective and use much less gas & power while driving than standard cars. Michelin, a tire manufacturer based in France, developed a tire-monitoring system using telematics and predictive analytics, providing a real-time view into the performance and wearing of specific tires on individual trucks to anticipate problems before they occur.
Impact Analysis of COVID-19 Pandemic
The report sheds light on the impact of the COVID-19 pandemic on the Autonomous Truck market growth with a detailed analysis. Our research framework covers the analysis on the ongoing COVID-19 outbreak featuring its impact on target industry, market players, revenue model, and potential paths forward. The research report delivers insights studying the major changes in consumer demand, purchasing behavior, dynamics of current market forces, and interventions of the local bodies and governments. The most authentic study covered in the research report gives a clear picture of the market patterns amid the pandemic period and suggestions for the market players to consider for the post-pandemic period.
Polaris Market research has segmented the autonomous truck market report on the basis of:
Autonomous Truck Autonomy Level Outlook (Revenue, USD Million, 2018 – 2027)
- Level 1
- Level 2
- Level 3
- Level 4
- Level 5
Autonomous Truck Sensor Type Outlook (Revenue, USD Million, 2018 – 2027)
Autonomous Truck Vertical type Outlook (Revenue, USD Million, 2018 – 2027)
- Construction & Manufacturing
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The regional section for the Autonomous Truck market size provides a detailed study of each major regional market across the globe. The report includes key details regarding the underlying patterns in the region with regards to the market and predicts the future trends for each regional market. Major data points such as import-export analysis, consumption volumes, cost of raw materials, production sites and volumes, downstream and upstream value chain analysis, and price trend analysis are used to forecast the possible scenario for the individual region.
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