zoox · Foster City, CA ·
Builds metrics, tools, and petabyte-scale distributed data pipelines to measure and validate the performance of Zoox's Collision Avoidance System for autonomous vehicles. Core tech is C++/Python plus large-scale distributed computing (Spark, Databricks, MapReduce).
Lucid Motors · Newark, CA
waymo · Mountain View, Ca, USA;San Francisco, Ca, USA
General Motors · Sunnyvale, California, United States of America
Compal · Farmington Hills, Michigan, United States
The Collision Avoidance System (CAS) is responsible for detecting and reacting to imminent collision situations in support of our vehicle’s overall safety goals. CAS Perception is responsible for processing raw sensor data from our vehicle’s world-class sensor suite using a combination of geometric, interpretable algorithms and deep learning to detect near-collisions with obstacles along our intended driving path, in the most challenging dense urban environments and under tight compute resource constraints. Overall CAS is parallel and complementary to our Main Artificial Intelligence (AI) autonomy stack, and has a close relationship with our vehicle hardware and safety teams in order to architect redundancy into our overall driving system.
The CAS Verification & Validation (CAS V&V) is a multidisciplinary team data, software and systems engineers defining and building metrics to measure the Collision Avoidance System performance and work with the Systems Design and Mission Assurance (SDMA) and QA teams to develop validation plans for the features.
General Motors · Sunnyvale, California, United States of America