MIT and Toyota develop intersection navigation model for AVs

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MIT and Toyota have designed a new model to help autonomous vehicles determine when it’s safe to merge into traffic at intersections with obstructed views.

The model uses its own uncertainty to estimate the risk of potential collisions or other traffic disruptions at intersections. It weighs several critical factors, including all nearby visual obstructions, sensor noise and errors, the speed of other cars, and the attentiveness of other drivers. Based on the measured risk, the system may advise the car to stop, pull into traffic, or nudge forward to gather more data.

“When you approach an intersection there is potential danger for collision. Cameras and other sensors require line of sight. If there are occlusions, they don’t have enough visibility to assess whether it’s likely that something is coming,” said Daniela Rus, director of MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL).

“In this work, we use a predictive-control model that’s more robust to uncertainty, to help vehicles safely navigate these challenging road situations.”

Researchers tested the system in more than 100 trials of remote-controlled cars turning left at a busy, obstructed intersection in a mock city, with other cars constantly driving through the cross street. Experiments involved fully autonomous cars and cars driven by humans but assisted by the system.

In all cases, the system successfully helped the cars avoid collision from 70-100% of the time, depending on various factors. Other similar models implemented in the same remote-control cars sometimes couldn’t complete a single trial run without a collision.

The model is specifically designed for road junctions in which there is no stoplight and a car must yield before manoeuvring into traffic at the cross street, such as taking a left turn through multiple lanes or roundabouts. Researchers split a road into small segments to help the model determine if any given segment is occupied to estimate a conditional risk of collision.

According to researchers, running the model on remote-control cars in real-time indicated that it is efficient and fast enough to deploy into full-scale autonomous test cars in the near future. However, the model still requires more rigorous testing before being used for real-world implementation in production vehicles.

The model would serve as a supplemental risk metric that an autonomous vehicle system can use to better reason about driving through intersections safely. The model could also potentially be implemented in certain ADAS, where humans maintain shared control of the vehicle.

Next, researchers aim to include other risk factors in the model, such as the presence of pedestrians in and around the road junction.