<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Projects &amp; Datasets on MOTIF Lab</title><link>https://www.motiflab.net/projects/</link><description>Recent content in Projects &amp; Datasets on MOTIF Lab</description><generator>Hugo</generator><language>en-us</language><atom:link href="https://www.motiflab.net/projects/index.xml" rel="self" type="application/rss+xml"/><item><title>OpenLKA: open dataset of lane keeping assist from market autonomous vehicles</title><link>https://www.motiflab.net/projects/openlka/</link><pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate><guid>https://www.motiflab.net/projects/openlka/</guid><description>&lt;h2 id="what-it-is">What it is&lt;/h2>
&lt;p>&lt;strong>OpenLKA&lt;/strong> is the first open benchmark dataset of lane-keeping-assist (LKA) behavior collected from a wide range of &lt;em>production&lt;/em> autonomous vehicles in real driving. Released in January 2025, it pairs CAN-bus signals with synchronized forward-facing video and lane-perception output, captured across multiple market vehicles under varied roadway and weather conditions in the Tampa, Florida region.&lt;/p>
&lt;h2 id="why-it-matters">Why it matters&lt;/h2>
&lt;p>Lane-keeping assist is one of the most widely deployed ADAS features, but its real-world performance is opaque: most public benchmarks are built on simulated environments or single-vehicle test fleets, and the behavior of production LKA systems differs widely between manufacturers. Without an open, multi-vehicle dataset, researchers cannot calibrate microsimulations of mixed-autonomy traffic, evaluate disengagement risk, or model the variance in LKA-equipped vehicle behavior that capacity and safety analyses now have to contend with.&lt;/p></description></item></channel></rss>