Driving Towards the Future: Deployments, Designs, and Challenges of Autonomous Vehicles

Quick Overview

The deployment of autonomous vehicles is currently in early stages, with about 2,000 truly driverless vehicles operating in North America, relying heavily on either highly engineered classical AV stacks or newer, less explainable end-to-end neural network architectures, all facing the persistent and critical challenge of reliably handling infinite edge cases.

Key Points: Currently, North America has roughly 2,000 truly driverless vehicles deployed, while worldwide there are less than 10,000 operating. The classical AV stack, used by Waymo and Zoox, is modular, allowing for causal analysis, but it is extremely engineering intensive, requiring large teams, like Waymo's 1500 software engineers. End-to-end neural architectures, exemplified by Tesla's FSD (version 12+), allow for fast, data-driven optimization but suffer from a lack of explanability and regressions when updated. A cutting-edge third design incorporates Large Language Models (LLMs) into end-to-end systems, enabling text input for instructions and queries, offering potential improvements in context understanding and 'open vocabulary' perception. A major remaining challenge for all AI systems is achieving the $10^{-7}$ failure rate required for human-level safety, as engineers typically aim for only 99% accuracy, which is insufficient for safety-critical tasks. The research lab discussed is developing a road hazard ontology grounded in real-world videos to systematically catalog and benchmark edge cases, finding that even models like GPT-5 recognize only about one-third of hazards (low recall). Human drivers require 24 lifetimes of driving to experience a fatality, indicating that short-term success with an automation system is insufficient to judge its ultimate safety level.

Context: This webinar, hosted by the Waterloo Center for Automotive Research (WAKCAR) and moderated by Michelle Van Djk, features Dr. Kristoff Teski, a Professor of Electrical and Computer Engineering at the University of Waterloo, who leads research on ensuring the safety of AI systems and driving behavior. Dr. Teski introduces the current state of autonomous vehicle deployments, contrasting the foundational software architectures being used, and detailing the significant challenges, particularly concerning safety validation and edge-case handling.