Creating driverless cars capable of humanlike reasoning is a long-standing pursuit of companies like Waymo, GM's Cruise, Uber, and others. Intel's Mobileye proposes a mathematical model -- the Responsibility-Sensitive Safety (RSS) -- it describes as a "common sense" approach to on-the-road decision-making that codifies good habits like giving other cars the right of way. For its part, Nvidia is actively developing Safety Force Field, a decision-making policy in a motion-planning stack that monitors unsafe actions by analyzing real-time sensor data. Now, a team of MIT scientists are investigating an approach that leverages GPS-like maps and visual data to enable autonomous cars to learn human steering patterns, and to apply the learned knowledge to complex planned routes in previously unseen environments. Their work -- which will be presented at the International Conference on Robotics and Automation in Long Beach, California next month -- builds on end-to-end navigation systems architected by Daniel Rus, director of the Computer Science and Artificial Intelligence Laboratory (CSAIL).