Rapid AI advances now enable engineers to develop autonomous driving technology faster than ever, but the true frontier of autonomous driving is the ability to couple those advances with demonstrable and rigorous safety.
Increasingly, depth and rigor is achieved not through the biggest budgets nor the largest fleets, but by distilling the most precise insights from real-world testing and simulation that assure autonomous systems can handle rare and unusual scenarios, the kind that may only occur once in a lifetime of driving.
Autonomous truck maker Kodiak has met this challenge by adopting two tools, including one we created with the help of AI, that accelerate the pace, depth and precision of our safety engineering. They go beyond traditional approaches and deliver clear, compelling evidence of the Kodiak Driver’s safety.
The first tool is Kodiak’s Probabilistic Risk Assessment (PRA). The PRA is a methodology Kodiak uses to estimate the expected rate of collisions of varying severities for the Kodiak Driver and to identify the key scenarios, risk factors, and autonomy failure modes most responsible for dominating the risk profile.
We then compare this output against human performance baselines, which we established in partnership with leading centers of transportation research.
The second is BreakPoint, an AI validation tool internally developed. BreakPoint hunts with intelligence and efficiency for edge cases that could result in collisions or other undesirable behavior.
The deep analysis capability provided by BreakPoint helps inform our PRA models. From this information flow, we precisely understand the key areas of risk for the Kodiak Driver and focus our efforts accordingly.
Together, these tools form core elements of our safety case and power our capital-efficient approach for safely developing and deploying our AI-powered autonomous driver in a variety of real-world environments and applications.
Collectively, Kodiak’s PRA and BreakPoint tooling represent critical cornerstones efforts to scalably deploy safe driverless vehicles.
Probabilistic risk assessment: Bringing a quantifiable dimension to safety
Autonomous vehicle safety cannot be merely claimed. It must be proven. The PRA is a method pioneered in other safety-critical industries, like aerospace and nuclear energy, for measuring safety risk.
The Kodiak PRA melds Bayesian probability theory, systems engineering, reliability analysis, and statistical models into quantified results. It acts as an inference engine that allows us to calculate expected rates of collision for scenarios that occur so rarely that they often could not be captured in real-world testing alone.
Critically, the PRA characterizes uncertainty associated with our risk assessment itself, allowing us to know, with mathematical rigor, where our evidence is strong and where it needs to grow. Hard numbers, not gut feelings.
In simple terms, the Kodiak PRA decomposes scenarios into three primary factors:
- Scenario exposure: How often does our vehicle encounter this type of operating scenario?
- Collision likelihood: Given that our vehicle encounters this operating scenario, how likely is it that a collision occurs?
- Severity of collisions: How severe would the collision in this scenario be?
The PRA accounts for inevitable uncertainties and incorporates new information as Kodiak collects more data and observations. So as we collect more data, the PRA updates to reflect our increased knowledge.
Practically managing AV risk
Functional safety tends to focus on “what happens when something breaks?” For autonomous vehicle safety, an even more challenging question to answer is, “Is my system capable of safely handling real-world scenarios even when everything is working as intended?”
The PRA method represents an iterative, living process to addressing autonomous vehicle safety, not a one-off box-checking exercise.
In that way, it is distinct from traditional functional safety processes and standards found in the automotive and trucking industries, where functional safety analyses are conducted once to validate safety and compliance, and then left static.
The most relevant standard for this class of behavioral safety is the Safety of the Intended Functionality (ISO 21448) standard, which addresses hazards caused by the system performing correctly but then encountering unexpected conditions.
