Model developed to help autonomous vehicles navigate traffic safely
MIT and Toyota researchers have designed a model designed to help autonomous vehicles determine when it’s safe to merge into traffic when the view is obstructed.
MIT and Toyota researchers have designed a model to help autonomous vehicles determine when it’s safe to merge into traffic at intersections with obstructed views.
Navigating intersections can be dangerous for driverless cars and humans alike. In 2016, roughly 23% of fatal and 32% of nonfatal U.S. traffic accidents occurred at intersections, according to a 2018 Department of Transportation study. Automated systems that help driverless cars and human drivers steer through intersections can require direct visibility of the objects they must avoid. When their line of sight is blocked by nearby buildings or other obstructions, these systems can fail.
The researchers developed a model that instead uses its own uncertainty to estimate the risk of potential collisions or other traffic disruptions at such intersections. It weighs several critical factors, including all nearby visual obstructions, sensor noise and errors, the speed of other cars, and even 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 the Computer Science and Artificial Intelligence Laboratory (CSAIL) and the Andrew and Erna Viterbi Professor of Electrical Engineering and Computer Science. “In this work, we use a predictive-control model that’s more robust to uncertainty, to help vehicles safely navigate these challenging road situations.”
The 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 to 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.
Modeling road segments
The model is specifically designed for road junctions in which there is no stoplight and a car must yield before maneuvering into traffic at the cross street, such as taking a left turn through multiple lanes or roundabouts. In their work, the researchers split a road into small segments. This helps the model determine if any given segment is occupied to estimate a conditional risk of collision.
Risk is visualized here by vertical bars. Higher vertical bars indicate higher likelihood that that specific spot in the intersection is occupied by another vehicle, so it’s unsafe to pull into the road. Instead, the vehicle must wait for a safe gap or nudge forward to gather more data. Courtesy: Massachusetts Institute of Technology[/caption]
Assistance and intervention
Running the model on remote-control cars in real-time indicates that it’s efficient and fast enough to deploy into full-scale autonomous test cars in the near future, the researchers say. (Many other models are too computationally heavy to run on those cars.) The model still needs far 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 “advanced driver-assistive systems” (ADAS), where humans maintain shared control of the vehicle.
Next, the researchers aim to include other challenging risk factors in the model, such as the presence of pedestrians in and around the road junction.
Massachusetts Institute of Technology (MIT)