A new machine-learning system may someday help driverless cars predict the next moves of nearby drivers, cyclists, and pedestrians in real-time.

Self-driving Roboats, developed at MIT, set sea in Amsterdam canals. If you don’t get seasick, an autonomous boat might be the right mode of transportation for you. 

Everyone knows the shortest distance between two points is a straight line. However, when you’re walking along city streets, a straight line may not be possible. How do you decide which way to go?

To get ahead of the uncertainty inherent to crashes, scientists from MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) and the Qatar Center for Artificial Intelligence (QCAI) developed a deep learning model that predicts very high-resolution crash risk maps.

MIT EECS professor and CSAIL principal investigator Hari Balakrishnan has received the annual SIGCOMM Lifetime Achievement Award, for his contributions to mobile and wireless systems, resilient networks, and congestion control.

A team from MIT has been working on a self-driving system that uses machine learning so that custom hand-tuning isn’t needed. Their new end-to-end framework can navigate autonomously using only raw 3D point cloud data and low-resolution GPS maps, similar to those available on smartphones today.

Leveraging research done on campus, student-run MIT Driverless partners with industry collaborators to develop and test autonomous technologies in real-world racing scenarios.

MIT researchers have developed a type of neural network that learns on the job, not just during its training phase. These flexible algorithms, dubbed “liquid” networks, change their underlying equations to continuously adapt to new data inputs. The advance could aid decision making based on data streams that change over time, including those involved in medical diagnosis and autonomous driving.