Production pipeline

Previsouly I talked about our workflows or automation, but I never took the time to explain our overall production pipeline. How do we manage the production of multiple experiments per year, each running on different simulation platforms? How do we capitalize on each project’s new features? How do we ensure everyone is working with up-to-date tools? Let’s explore under the hood and see how we get all of that done.

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We need better mice

This post has nothing to do with Unreal, driving simulation, or anything we usually talk about around here. It’s going to be just me, complaining about the computer mice market and why I think we need better mice, for developers and beyond.

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Post Mortem #1: Level Blueprint

We’re nearing the completion of one of our largest driving simulation experiment ever, which also was the first Unreal project for my colleagues involved and for our driving simulator (SIMAX). So let’s take some time to share what we learned from it, especially the non-trivial bits. Our first post will cover our use of the Level Blueprint.

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What we've been up to, August 2023

It’s the middle of summer, everything is going much slower than usual, so let’s take that time to look back at the past three months, shall we? I’m not going to lie, I wasn’t very productive… But still, some interesting things happened.

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Anatomy of a scenario: snowy mountain road

A while ago, a researcher at my lab asked me: “Now that we’re using Unreal Engine, could we have things like mountain roads? Maybe even with snow?”. The answer is yes, and in this blog entry, I’ll explain how I implemented this short demo.

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I hate car mirrors

Driving simulation development is fun and all, right until you remember that actual cars have mirrors. So today I’ll rant about that, and maybe explore solutions.

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Angular size of partially occluded actors

A researcher colleague recently asked me: “Could we get the angular size of an actor in realtime, even if they’re partially occluded?”. As with many things in Unreal Engine, the answer is “yes we can!”, so here’s a breakdown of our answer to this question.

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StreamDeck for Driving Simulation Experiments

I’m always on the lookout for new and often out of place tools that could find their role in our research workflows. One example is Veyon: built for classroom computers management, it also happens to be perfect for managing nDisplay clusters, at the core of our simulators. StreamDeck are a recent addition to our fleet, and today we’ll discuss all the use cases in which those will greatly help.

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Workflow in progress #2

Second entry in a series that takes a closer look at how we actually get our driving simulation experiment from paper to the simulator. This time, we focus on a project where the workflow ended up being very different from our previous entry.

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Workflow in progress #1

What’s it like using Unreal Engine to actually implement a driving simulation experiment? What does that workflow look like? Well, we’re kind of learning as we go along, and this post is a first of a series that will look a bit closer at how things go from paper to simulator.

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Scenario Authoring

We’ve already talked about scenarios, which are at the very core of driving simulation. But this was a rather high level overview, and today I want to go a bit deeper to discuss how we’re actually implementing our scenarios, and how we try to make the process as easy and efficient as possible

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Live VR eye-tracking data visualization

Eye-tracking is widely used to study driver’s behavior in simulated environments. However, we’re used to either have glasses (e.g. Pupil Core) or fixed setups (e.g. Smart Eye Pro); and the newly released VR headsets with included eye-tracking (e.g. Vive Pro Eye) bring both new opportunities and challenges. Today, we’ll talk about live visualization of eye-tracking data, why and how we managed to implement it in our platform.

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Unreal Marketplace products for Driving Simulation

For us, one of the major benefit of using Unreal Engine is its Marketplace. It offers the ability to purchase so many products, at very reasonable prices, and that work out-of-the-box. It saved us countless amount of time and money, compared to our previous workflows. However, finding the right products for your needs is not always easy, so I thought I’d share the list of product we own, our favorites, and some comments on them.

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Anatomy of a Scenario

Recently I shared a short scenario I made, involving an e-scooter, a crowd, a bus and an unfortunate ending. It’s definitely not a usual scenario from our “driving-oriented” perspective, so I thought it’d be interesting to explain how it came to life.

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What we've been up to, August 2021

What have we been up to in the past couple of months? What’s new in the V-HCD world? This post is a first of a hopefully long series where we showcase the new things in our driving simulation platform.

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Automate the Boring Stuff

In the past months, the language I’ve used the most in the V-HCD is PowerShell. Considering the V-HCD is powered by Unreal Engine, which has support for Blueprint, C++ and Python; you might wonder why do I write PowerShell scripts. The answer is: to automate the boring stuff. And there’s a lot of it.

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VR Headsets

For the past ten years, the world of Virtual Reality headsets has been redefined, lead by Facebook’s Oculus and HTC’s Vive. Those relatively cheap devices allow for better immersion in various types of environments, and are now being used in a wide range of both industry and research entities. But, even though we’ve previously mentioned use of CAVE simulators, we never talked about VR headsets. Why is that?

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Why not CARLA?

CARLA is an open-source simulator for autonomous driving research. In addition to open-source code and protocols, CARLA provides open digital assets (urban layouts, buildings, vehicles) that were created for this purpose and can be used freely. The simulation platform supports flexible specification of sensor suites and environmental conditions.

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Data Collection

The main point of a driving simulation experiment is to collect data relevant to our study. This includes both simulation related data (e.g. speed), but also physiological data, such as eye-tracking or heart rate; all of which need to be synchronized. Today we’ll discuss how we solve this in our platform.

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Scenario Variants

The basis of a driving simulation experiment is the scenario. But quite often, an experiment requires not one but multiple scenarios. And most often, those multiple scenarios can be described as some kinds of variants from one another. How do we structure our experiment around this variant concept?

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nDisplay

nDisplay is Unreal Engine’s tool for CAVE and other cluster rendering, allowing simulation to be displayed on any amount of screens from any amount of computers. However, getting nDisplay to work for driving simulation can be challenging.

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Version control

As a software engineer, version control is mandatory for any project I work on. A driving simulation platform, or experiment, is no exception. However, such projects have quite a few differences from traditional software. In this post, we’ll explain the challenges we faced regarding source control, and how we answered them (or failed to).

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Making a scene

When you start implementing your driving simulator experiment, the first thing you’ll work on is probably the scene. You may want to reuse an existing one from a previous project, maybe modify it, or start from scratch to build something tailored to your needs.

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Virtual Driver

In most driving simulation scenarios, there are two types of cars. The one that’s driven by the participant (commonly referred to as ego), and others that are controlled from the scenario. With the rise of autonomous driving, it’s not uncommon to have scenarios where ego also is controlled from the scenario. How do we control all those cars, making sure that their external behavior appears realistic, all the while being easy to configure for researchers building their experiment?

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Scenarios

An important and critical stage in driving simulator experiments is scenario authoring. We want to offer as much control as possible to researchers, allowing them to build any experiment they can imagine. But we also want this process to be as easy and intuitive as possible, so that non-experts can start working on their scenario as early as possible in the experiment design phase, allowing for quick and iterative development.

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