COVID-19 has had a significant impact on the world, affecting people's lives and forcing many businesses to suspend their operations. At Waymo, we're actively monitoring the situation, taking steps to support our local communities, and contributing to COVID-19 response efforts. While Waymo has temporarily suspended its on-the-road operations as we put the health and safety of our riders, partners, and employees first, we are still driving our technology forward with our work in simulation. Gaining 100 years of experience in one day Simulation is vital in the advancement of self-driving technology. At Waymo, one day in simulation is like driving more than 100 years in the real world.
Simulation-based virtual testing has become an essential step to ensure the safety of autonomous driving systems. Testers need to handcraft the virtual driving scenes and configure various environmental settings like surrounding traffic, weather conditions, etc. Due to the huge amount of configuration possibilities, the human efforts are subject to the inefficiency in detecting flaws in industry-class autonomous driving system. This paper proposes a coverage-driven fuzzing technique to automatically generate diverse configuration parameters to form new driving scenes. Experimental results show that our fuzzing method can significantly reduce the cost in deriving new risky scenes from the initial setup designed by testers. We expect automated fuzzing will become a common practice in virtual testing for autonomous driving systems.
You could argue that Waymo, the self-driving subsidiary of Alphabet, has the safest autonomous cars around. It's certainly covered the most miles. But in recent years, serious accidents involving early systems from Uber and Tesla have eroded public trust in the nascent technology. To win it back, putting in the miles on real roads just isn't enough. So today Waymo not only announced that its vehicles have clocked more than 10 million miles since 2009.
In this paper, we present ViSTA, a framework for Virtual Scenario-based Testing of Autonomous Vehicles (AV), developed as part of the 2021 IEEE Autonomous Test Driving AI Test Challenge. Scenario-based virtual testing aims to construct specific challenges posed for the AV to overcome, albeit in virtual test environments that may not necessarily resemble the real world. This approach is aimed at identifying specific issues that arise safety concerns before an actual deployment of the AV on the road. In this paper, we describe a comprehensive test case generation approach that facilitates the design of special-purpose scenarios with meaningful parameters to form test cases, both in automated and manual ways, leveraging the strength and weaknesses of either. Furthermore, we describe how to automate the execution of test cases, and analyze the performance of the AV under these test cases.
For decades, anyone who wanted to know whether a new car was safe to drive could simply put it through its paces, using tests established through trial and error. Such tests might investigate whether the car can take a sharp turn while keeping all four wheels on the road, brake to a stop over a short distance, or survive a collision with a wall while protecting its occupants. But as cars take an ever greater part in driving themselves, such straightforward testing will no longer suffice. We will need to know whether the vehicle has enough intelligence to handle the same kind of driving conditions that humans have always had to manage. To do that, automotive safety-assurance testing has to become less like an obstacle course and more like an IQ test.