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Highways England has teamed up with Loughborough University to ensure Britain’s motorways can accommodate connected and autonomous vehicles (self-driving) vehicles.

As part of a £1m research, the scientist will look at operations at roadworks, merging and diverging sections (across lanes and at junctions) and lane markings to understand the challenges connected and autonomous vehicles (CAVs) may face.

The project, named CAVIAR (Connected and Autonomous Vehicles: Infrastructure Appraisal Readiness), is being carried out in partnership with a construction company, Galliford Try. CAVIAR was announced as a winner in Highways England’s innovation and air quality competition last year and awarded £1m from the government company’s innovation and modernisation designated fund.

According to the statement of the Loughborough University, the British government and industry are investing heavily in Connected and Autonomous Vehicle (CAV) technology as they compete to attain a competitive advantage in the future market for mobility systems.

The ability of CAVs to operate fully autonomously may not be entirely contained within the vehicle technology due to the inherent complexity in the roadway infrastructure.

In addition, weather conditions may limit the ability of on-board sensors to detect road markings, configurations, traffic and road conditions.

Professor of Intelligent Transport Systems, Mohammed Quddus, the principal investigator on the project, and also of ABCE, said: “To date there is significant investment and advancement in Connected and Autonomous Vehicles.

It is, however, not known whether existing road infrastructure, which was designed for conventional vehicles, is ready for the safe and efficient operations of CAVs.

CAVIAR directly addresses this challenge.

Although CAVs are designed with existing infrastructure in mind, ensuring they are safe to operate on motorways will require evaluating how road layouts affects their operational boundaries such as their ability to sense lanes and make appropriate decisions.”

The platform will be employed to evaluate whether CAVs can safely navigate through the existing configurations of construction zones.

Real-world data from different lane configurations will be collected and fed into the simulation models to calibrate and examine how CAVs respond to dynamic lane changes.

Digital maps representing dynamic lane configurations will be transmitted to CAVs in advance for informed routing decisions.

In terms of lane markings, the platform will be utilised to understand how environmental conditions affect a CAVs ability to detect lane markings, such as snow, and low lighting – for example at night.

For merging and diverging scenarios, inconsistencies in geometric configurations will be appraised to examine whether CAVs are able to merge safely from the local road network (low speed) to the motorway network (high speed).

CAVIAR objectives are:

  1. To instrument infrastructure and vehicle for acquiring relevant data
  2. To create a centralised data integration architecture
  3. To build simulation models for CAV failure scenarios
  4. To verify the experimental and simulation platform by feeding data from live trials to the simulation models and vice versa
  5. To appraise motorway readiness level for safe and efficient CAV operations

Photo is illustration, source: Twitter/ Tesla 

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