The Verge explores the challenges of achieving autonomous driving in trucks compared to rideshare vehicles. While trucks were initially seen as an ideal application due to predictable freeway driving, the complexities of high speeds, size, and unique challenges on freeways make autonomous trucking harder than anticipated. Unlike rideshare vehicles, trucks face issues such as stopping distance vs. sensing range, complex controls due to separate truck and trailer movement, and specific challenges on freeways, including achieving minimal risk conditions and dealing with boring, controlled-access environments.
The article highlights the difficulty in meeting the high safety standards required for autonomous driving, where the system must make all safety-critical decisions independently, exceeding human safety levels. Sensing solutions face challenges in meeting the requirements for trucks, and controlling trucks proves more intricate than passenger vehicles.
Despite the challenges, the author believes that advancements in sensing, machine learning, and persistent efforts by companies like Aurora, Kodiak, and Gatik will eventually lead to successful autonomous trucking deployments, albeit not reaching a million-mile deployment in the near future. The focus on scaled freeway rideshare is anticipated after achieving success in city rideshare applications.