By Tech Brew Staff
less than 3 min read
Definition:
A decision tree is a type of machine-learning algorithm that, per IBM, “is utilized for both classification and regression tasks.” Its tree-like structure, similar to a flowchart, “consists of a root node, branches, internal nodes, and leaf nodes.” The leaf nodes “represent all the possible outcomes within the dataset.”
Some of the advantages of decision trees, according to IBM, are that they’re easy to interpret and tend to be more flexible than other algorithms. However, there are downsides, like being more expensive to train relative to other algorithms.
Decision trees are used in autonomous vehicles to help AV systems make driving decisions. In autonomous driving, decision trees can help optimize routes, detect and respond to faulty inputs, and interpret sensor data to accurately identify surrounding objects.
AVs, after all, are constantly taking in new data—via cameras, GPS, radar, and other sensors, often paired with AI capabilities—about their surroundings, interpreting and analyzing it, and deciding whether to stop, turn, slow down, speed up, etc. Tech Brew has seen this decision-making in action during test rides that included scenarios like interacting with human-driven vehicles in parking lots, encountering garbage trucks, and approaching obstructions in the roadway.
The complexity of real-world driving is one of the factors that has held the autonomous-vehicle market back from scaling to the point that many had predicted it would be at today, even as incremental progress continues on deployments of robotaxis and other types of AVs.
“Decision Trees can assist vehicles in making sequential decisions and ultimately deriving appropriate driving strategies through the evaluation of multiple conditions and the top-down ‘polling’ mechanism,” according to a 2025 research paper in ScienceDirect, which notes applications of decision tree models in autonomous driving such as evaluating the risk of a crash or making decisions about changing lanes on highways.
