<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>ADAS | Chenzhu Wang</title><link>https://chenzhuwang.com/tags/adas/</link><atom:link href="https://chenzhuwang.com/tags/adas/index.xml" rel="self" type="application/rss+xml"/><description>ADAS</description><generator>Hugo Blox Builder (https://hugoblox.com)</generator><language>en-us</language><lastBuildDate>Sun, 26 Apr 2026 00:00:00 +0000</lastBuildDate><image><url>https://chenzhuwang.com/media/icon_hu7729264130191091259.png</url><title>ADAS</title><link>https://chenzhuwang.com/tags/adas/</link></image><item><title>Multimodal ADAS Video Safety</title><link>https://chenzhuwang.com/project/multimodal-adas-video/</link><pubDate>Sun, 26 Apr 2026 00:00:00 +0000</pubDate><guid>https://chenzhuwang.com/project/multimodal-adas-video/</guid><description>&lt;p>This project line studies how first-person traffic video, social media crash evidence, and ADAS-equipped vehicle events can support richer safety understanding.&lt;/p>
&lt;p>Current work includes the SAVeD dataset for near-miss and crash event analysis, LLM-assisted event parsing, vision-language modeling, and controllable diffusion-based counterfactual scenarios for &amp;ldquo;what-if&amp;rdquo; safety assessment.&lt;/p></description></item><item><title>SAVeD: A first-person social media dataset for ADAS-equipped vehicle near-miss and crash event analysis</title><link>https://chenzhuwang.com/publication/preprint/</link><pubDate>Thu, 01 Jan 2026 00:00:00 +0000</pubDate><guid>https://chenzhuwang.com/publication/preprint/</guid><description>&lt;p>This work anchors the multimodal traffic video and ADAS safety theme. It supports event parsing, near-miss/crash understanding, and future counterfactual safety analysis.&lt;/p></description></item></channel></rss>