I’m in a waymo autonomous vehicle traveling down a two-lane residential street. Just why not for two cars and there’s a double parked vehicle in front of us so the vehicle starts to swerve into the opposing direction of traffic when it kind of suddenly swers back behind the blocking vehicle. So in thinking about this, it seems that their sensors when it was starting to enter into opposing lane of traffic. So outside of kind of its its usual envelope then it received it. Maybe it misjudge or the opposing vehicles traveling at a higher direction. Anyway, something happened that it had the correct for and I’m wondering if that’s what tends to get these vehicle stuck. So let’s say there was a long line of traffic and it required a really aggressive but not dangerous. Merge into opposing traffic knowing that nobody’s going to hit you head on at 25 mph > [!ai]+ AI > > You’ve described a common scenario that highlights some of the challenges faced by autonomous vehicles (AVs) like Waymo, and you’re right to point out the potential for these situations to lead to the vehicle getting “stuck” or making unexpected maneuvers. Let’s break down what likely happened and why it’s a difficult problem for AVs to solve: > **What Likely Happened** > 1. **Initial Assessment:** The Waymo vehicle correctly identified the double-parked car as an obstacle and initiated a lane change maneuver to go around it. This is standard behavior. > 2. **New Information/Unexpected Event:** As it began to merge into the opposing lane, one of the following likely occurred: > - **Sensor Input:** Its sensors detected an oncoming vehicle approaching faster than initially anticipated. > - **Prediction Error:** The AV’s prediction model initially underestimated the oncoming vehicle’s speed or trajectory. > - **Conservative Programming:** Waymo vehicles are programmed to be very cautious. Even a slight increase in perceived risk can trigger a return to a safer position. > - **Edge Case:** The specific combination of factors (speed of oncoming car, distance, road geometry, etc.) might have fallen into an “edge case” scenario that the system wasn’t fully trained to handle. > 3. **Corrective Action:** The AV determined that continuing the lane change was too risky and abruptly returned to its original lane behind the double-parked car. This is a safety mechanism kicking in. > **Why This Can Lead to “Stuck” Situations** > You’re spot on that in a scenario with a long line of oncoming traffic, this cautious behavior could lead to the AV being unable to complete the lane change. Here’s why: > - **Aggressive vs. Safe:** Human drivers often rely on subtle cues and social negotiation (eye contact, slight movements) to merge into traffic, even in tight spots. They can make judgment calls about what’s “aggressive but safe.” AVs are currently programmed to prioritize safety above all else, making them less likely to take risks that a human might deem acceptable. > - **Limited “Understanding”:** While AVs are getting better, they still lack the nuanced understanding of social dynamics and implicit rules of the road that humans possess. They may not recognize when an oncoming driver is likely to yield or adjust their speed. > - **Over-Reliance on Sensors:** AVs primarily rely on their sensors (lidar, radar, cameras) to perceive the world. These sensors are excellent at measuring distances and speeds, but they don’t provide the same level of contextual information that a human driver gets from observing the overall traffic flow and driver behavior. > - **Training Data Limitations:** AVs are trained on vast datasets of driving scenarios, but it’s impossible to cover every possible edge case. The real world is incredibly complex and unpredictable. > **The Challenge of “Aggressive but Safe” Merging** > Your example of needing an “aggressive but safe” merge in a long line of traffic perfectly illustrates the core challenge. This requires: > - **Accurate Prediction:** The AV needs to accurately predict the behavior of multiple oncoming vehicles, not just the closest one. > - **Risk Assessment:** It needs to make a sophisticated risk assessment, balancing the need to proceed with the need to avoid a collision. > - **Social Negotiation (Future):** Ideally, future AVs might be able to communicate their intentions to other drivers (e.g., through external signals) to facilitate smoother merges. > **In Conclusion** > The behavior you observed in the Waymo vehicle is a result of its safety-first programming and the inherent difficulties of navigating complex, dynamic environments. While AVs are making significant progress, scenarios requiring “aggressive but safe” maneuvers remain a major challenge. Overcoming these challenges will require continued advancements in sensor technology, prediction algorithms, and potentially even the ability for AVs to engage in some form of social negotiation with human drivers.