Automatic lane change scenario extraction and generation of scenarios in OpenX format from real-world data

by   Dhanoop Karunakaran, et al.

Autonomous Vehicles (AV)'s wide-scale deployment appears imminent despite many safety challenges yet to be resolved. The modern autonomous vehicles will undoubtedly include machine learning and probabilistic techniques that add significant complexity to the traditional verification and validation methods. Road testing is essential before the deployment, but scenarios are repeatable, and it's hard to collect challenging events. Exploring numerous, diverse and crucial scenarios is a time-consuming and expensive approach. The research community and industry have widely accepted scenario-based testing in the last few years. As it is focused directly on the relevant critical road situations, it can reduce the effort required in testing. The scenario-based testing in simulation requires the realistic behaviour of the traffic participants to assess the System Under Test (SUT). It is essential to capture the scenarios from the real world to encode the behaviour of actual traffic participants. This paper proposes a novel scenario extraction method to capture the lane change scenarios using point-cloud data and object tracking information. This method enables fully automatic scenario extraction compared to similar approaches in this area. The generated scenarios are represented in OpenX format to reuse them in the SUT evaluation easily. The motivation of this framework is to build a validation dataset to generate many critical concrete scenarios. The code is available online at


page 1

page 3

page 4

page 6


Parameterisation of lane-change scenarios from real-world data

Recent Autonomous Vehicles (AV) technology includes machine learning and...

The ConScenD Dataset: Concrete Scenarios from the highD Dataset According to ALKS Regulation UNECE R157 in OpenX

With Regulation UNECE R157 on Automated Lane-Keeping Systems, the first ...

Generating and Explaining Corner Cases Using Learnt Probabilistic Lane Graphs

Validating the safety of Autonomous Vehicles (AVs) operating in open-end...

Unsupervised Lane-Change Identification for On-Ramp Merge Analysis in Naturalistic Driving Data

Connected and Automated Vehicles (CAVs) are envisioned to transform the ...

Querying Labelled Data with Scenario Programs for Sim-to-Real Validation

Simulation-based testing of autonomous vehicles (AVs) has become an esse...

SceML - A Graphical Modeling Framework for Scenario-based Testing of Autonomous Vehicles

Ensuring the functional correctness and safety of autonomous vehicles is...

Please sign up or login with your details

Forgot password? Click here to reset