One-year feasibility study processing existing datasets, to understand the parameters needed for modelling human drivers and how to extend them to make vehicle rules for CAVs, improving current technology and modelling impacts to balance comfort, capacity and safety. The aim is to ensure CAV behaviour meets the needs of both regulators and customers.
LAMBDA-V is a one-year study on the feasibility of processing existing massive datasets, to understand the parameters needed for modeling human drivers and how to extend them to make vehicle rules, improving current technology and modeling impact to balance comfort, capacity and safety. This could ensure CAV behaviour meets the needs of both regulators and customers.
The project focused on innovative exploration of a full end-to-end data chain and business model in a mixed fleet environment. This integrated vehicle maker and road operator perspectives on CAV behavior; and examined how to develop privacy-law-compliant datasets for other CAV projects. It brought together CAV and modeling software developers with data from massive mixed fleets of anonymized drivers across the UK, rather than small fleets of specialized vehicles in one location.
New rules for safer and more efficient driving behavior may be built from data from existing vehicles, based not just on road laws but on how humans drive vehicles in specific circumstances. These could be ‘tuned’ by modeling how CAVs and other vehicles then behave in a mixed fleet, which will help to tailor early CAV behavior to match that of human drivers and thereby improve confidence for early adopters.
Aimsun used an existing model of Birmingham and then simulated the closure of various parts of the network examining the impact on network speeds and throughputs. These closures were used as a surrogate for situations where a Level 5 driverless vehicle were to break down or be incapable of proceeding and require additional control from a remote operations centre.
Aimsun was able to accurately simulate the effects of introducing unforeseen impacts on traffic while the project as a whole examined patents on rules for CAVs; an improved understanding of early mixed fleet operation of human and automated vehicles and how to make early level self-driving vehicles attractive to users. Additionally, improved understanding for highways authorities and vehicle makers was delivered regarding how to deploy CAVs on a variety of real-world roads.
The key output was the identification of potential product improvements for all partners to make data, modeling and rules generate new sales. The benefits would include: reduced unforeseen impacts on traffic; patents on rules for CAVs; an improved understanding of early mixed fleet operation of human and automated vehicles and how to make early level self-driving vehicles attractive to users; and improved understanding for highways authorities and vehicle makers regarding how to deploy CAVs on a variety of real-world roads.