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Research on road works and improving traffic flow  using V2I and V2V with RSU able to signal over 2kms 

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Research on road works and improving traffic flow  using V2I and V2V with RSU able to signal over 2kms 

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https://pdfs.semanticscholar.org/b58b/72b4111aebc02cc71886f50736b1e67339b9.pdf

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A vehicle-to-infrastructure communication basedalgorithm for urban traffic control

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A vehicle-to-infrastructure communication basedalgorithm for urban traffic control

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We present in this paper a new algorithm for urban traffic light control with mixed traffic (communicating and non communicating vehicles) and mixed infrastructure (equipped and unequipped junctions). We call equipped junction here a junction with a traffic light signal (TLS) controlled by a road side unit (RSU). On such a junction, the RSU manifests its connectedness to equipped vehicles by broadcasting its communication address and geographical coordinates. The RSU builds a map of connected vehicles approaching and leaving the junction. The algorithm allows the RSU to select a traffic phase, based on the built map. The selected traffic phase is applied by the TLS; and both equipped and unequipped vehicles must respect it. The traffic management is in feedback on the traffic demand of communicating vehicles. We simulated the vehicular traffic as well as the communications. The two simulations are combined in a closed loop with visualization and monitoring interfaces. Several indicators on vehicular traffic (mean travel time, ended vehicles) and IEEE 802.11p communication performances (endto-end delay, throughput) are derived and illustrated in three dimension maps. We then extended the traffic control to a urban road network where we also varied the number of equipped junctions. Other indicators are shown for road traffic performances in the road network case, where high gains are experienced in the simulation results

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https://www.its.dot.gov/research_archives/safety/v2i_comm_plan.htm

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Potential V2I Safety Applications crash scenario mitigation opportunities include:

Intersection safety

Roadway departure prevention

Speed management

Transit safety and operations

Commercial vehicle enforcement and operations

At-grade rail crossing operations

Priority assignment for emergency vehicles

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-  Bounding boxes
- Feature Maps
- Filters (Kernels)
- Over-fitting
- Point Clouds
- Receptive Field
- Sensor Fusion
- Lidar Augmentation

# data[].attributes.body
An area of the body surface over which a single sensory receptor, or its afferent nerve fiber, is capable of sensing stimuli. In some body area, e.g. face, ears, front paws, the sensitive areas are small; over the back they are larger.

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A collection of data points defined by a given coordinates system. In a 3D coordinates system, for example, a point cloud may define the shape of some real or created physical system.

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A modeling error which occurs when a function is too closely fit to a limited set of data points. Over-fitting the model generally takes the form of making an overly complex model. In reality, the data being studied often has some degree of error or random noise within it.

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A small matrix used to apply effects like the ones you might find in Photoshop or Gimp, such as blurring, sharpening, outlining or embossing.

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A rectangular area to efficiently cover all the affected area of some random shape. In the context of autonomous vehicles, the term is short for axis-aligned bounding box. It can be used to efficiently and effectively represent the area of collision to be avoided from two or more autonomous vehicles.

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Sensor fusion is the aggregation of data from multiple sensors to gain a more accurate picture of the sensors' subject or environment than can be determined by any one sensor alone

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Time-of-flight sensor where a laser-pulse is fired and the reflected pulse is recorded by a diode, the distance is then estimated as the velocity of light divided by times the time it takes for the laser pulse to return.
Prone to errors. One example is a situation where, on a road, there are autonomous vehicles which all use the lidar augmentation. This would result in vehicles processing incorrect 3D models of the surrounding environment.
Encrypting the laser-pulse so that it is unique to a single vehicle

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3D mapping of a room using a rotating LIDAR-Lite v3. The mounting is 3D-Printed and the Lidar is driven by one stepper motor and a servo, controlled by an Arduino Uno. Map rendered using Unity3D.