Autonomous Transport Systems
Autonomous transport systems provide unmanned, autonomous transfer of equipment, baggage, people, information or resources from point-to-point with minimal intervention. They can include the full range of transport vehicles, including trucks, buses, trains, metros, ships, and airplanes. They are most commonly deployed in controlled industries zones but are expected to soon be deployed in public areas with varying degrees of autonomy. We differentiate autonomous transport systems from autonomous vehicles. Whereas autonomous vehicles serve individual passengers (who may or may not own the vehicle), autonomous transport systems are interconnected fleets of vehicles owned by a business to service a particular need systematically. When discussing autonomous transport systems, the focus is on the interaction among vehicles in a sophisticated system that interfaces with ERP, MES, and other enterprise data management systems. The autonomy of the vehicle is one component of a larger interconnected system of autonomous and semi-autonomous activity with the objective of achieving business or organizational objectives, such as delivering the mail or moving soil from a mine to a processing facility.
- INDUSTRIES
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Automotive Transportation - FUNCTIONS
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Logistics & Warehousing - CASE STUDIES
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Axter AGV: Special AGV - China - AxterThe client faces complex material handling due to non-standard load dimensions and weight, increasing safety risks and load handling damages.
Telit: Telit | Jooycar Offering A New Solution For The Usage-Based Insurance MarketJooycar sought to provide an end-to-end, cloud-based insurance telematics platform focused on creating value insurers and enhancing policyholders’ experience. Its usage-based insurance (UBI) solution demanded various data inputs and outputs, including automotive data collection, user data collection and output to many possible data bases and applications. To create such a flexible smart car platform, Jooycar enlisted the help of Telit’s IoT technology and expertise.
Intel: Employing Intel Deep Learning SDK Toward Bettering Image Recognition ModelsIn this case study, the challenge explored involves LeNet*, one of the prominent image recognition topologies for handwritten digit recognition. In the case study, we dive into how the training tool can be used to visually set up, tune, and train the Mixed National Institute of Standards and Technology (MNIST) dataset on Caffe* optimized for Intel® architecture. Data scientists are the intended audience. - MARKET SIZE
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The semi-autonomous truck market is estimated to be 260,000 units in 2018 and is projected to reach 1,130,000 units by 2025, at a CAGR of 23.38%.
The autonomous truck market is estimated to be 15,000 units in 2025 and is projected to reach 81.8 thousand units by 2030, at a CAGR of 39.96%.
Source: Businesswire
The autonomous train market, in terms of volume, is projected to grow at a CAGR of 4.87% from 2018 to 2030. The market is estimated at 54,558 Units in 2017 and is projected to reach 106,290 Units by 2030. In this study, 2017 has been considered the base year, and 2018–2030 is the forecast period, for estimating the market size of the market.
Source: Markets & Markets
- BUSINESS VIEWPOINT
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Where are autonomous transport systems used?
The first stage of development testing for autonomous transport systems is in controlled industrial environments such as mines and ports. These areas have well-defined paths and limited variability in the types of obstacles that a vehicle must navigate around. Control of the environment by a single corporation also means that many vehicles are connected to the same system. This increases the ability of a vehicle to foresee potential obstacles and to coordinate action with other vehicles in the system.
Autonomous trains, subways, ships, and airplanes are also being experimented with. Autonomous trains and subways are commonly used today and are significantly easier to manage due to the high degree of system control. Ships and airplanes can and do run autonomously for long durations but are typically not operated without a human operator at the controls. We are moving into a period where the presence of human operators will likely remain primarily to mitigate legal risk rather than with the anticipation that humans will be superior at piloting the vehicle in most circumstances.
Systems for general road use are being developed rapidly. Highways are widely expected to be the first public roads to see significant autonomous traffic. As opposed to urban roads, highways typically offer a wide field of vision and relatively homogenous behavior among other objects of the road. Urban roads must contend with broken tree limbs, children playing, bikes running stop signs, people pulling out of driveways, and other unpredictable factors.
- STAKEHOLDER VIEWPOINT
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- TECHNOLOGY VIEWPOINT
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How are autonomous transport systems connected to information systems?
Data distribution service (DDS) is an industry standard for autonomous vehicles because it is open standard and cross-vendor.
- DATA VIEWPOINT
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- DEPLOYMENT CHALLENGES
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What are the technical challenges facing autonomous transport systems?
Technical challenges related to autonomy include ensuring reliable data ingestion, guaranteeing real-time response in complex situations, managing complex data flows between vehicles and connected systems, integrating systems, and securing system access to prevent the hacking of vehicles or systems.
