Transportation
❯
Autonomous driving
❯AI components for vehicle platooning on public roads
Autonomous apron truck
For:
Transporter
Scope:
Automated transportation of luggage (carts) to requested destinations on an airport apron while following local traffic rules and resolving unplanned
conflicts.
Goal:
Improve Operation Efficiency
Government
Defense & Military - Autonomous Vehicle
For:
Military Operatives.
Scope:
Using computer vision to implement perimeter safety for Autonomous Vehicles.
Goal:
Improved Product Development / R&D
AI components for vehicle platooning on public roads
Scope:
Trains of vehicles that drive very close to each other at nearly equal speed
(platoons) on public roads, in particular platooning trucks on motorways.
Goal:
Improve Operation Efficiency
Autonomous trains (Unattended train operation (UTO))
Scope:
Freight and passenger trains operate autonomously, excluding any crew
presence on board, but with remote operator attention involved.
Goal:
Improve Operation Efficiency
Self-driving aircraft towing vehicle
Scope:
Self-Driving towing vehicle for aircrafts, operating on an airfield autonomously.
Goal:
Improve Operation Efficiency
AI components for vehicle platooning on public roads
Goal:
Improve Operation EfficiencyProblem addressed
The objectives of truck automation are energy saving and enhanced
transportation capacity by platooning, and eventually possible reduction of
personnel costs by unmanned operation of following vehicles. In a variant of this
concept, platoons of passenger cars follow a truck autonomously.
Scope of use case
Trains of vehicles that drive very close to each other at nearly equal speed
(platoons) on public roads, in particular platooning trucks on motorways.
Description
A major development in research on intelligent
transportation systems (ITS) is cooperative adaptive cruise
control (CACC). It takes adaptive cruise control (ACC) to the
next level by adding direct communication between vehicles.
Directly communicating accurate state information allows
vehicles to drive much closer to each other without
compromising safety. This is the basis of platooning: trains
of vehicles that drive very close to each other at nearly equal
speed. By CACC, platoons become string stable: changes in
the acceleration or deceleration are reduced by the following
vehicles instead of being amplified. This property is expected
to greatly improve the throughput of vehicles on highways,
because it is exactly the amplification of acceleration and
deceleration that causes many traffic jams. Research and
development on truck platooning is driven partially by the
potential fuel savings and the expectation of an attractive
return on investment.
Implementations of platooning are complex cyber-physical
systems [22]. In freight transportation, for example, a typical
system architecture consists of the fleet layer, the
cooperation layer, and the vehicle layer. AI components are
already used on the vehicle layer (e.g. lane keeping), future
products are likely to incorporate AI solutions on several
functional levels and all system layers.
Lane keeping is an established AI technology in the
automotive industry [25]. Some examples for other potential
AI components in platooning systems are:
prediction of behaviour of surrounding traffic [23];
controllers for platooning strategies ([20], [22]);
road surface recognition [21];
driver state assessment ([26], [30]);
safe control and safety regions [24].
Sensor Network - IOT
Live Video
AI: Perceive