Transportation
Collaborative Research: CPS: Medium: An Online Learning Framework for Socially Emerging Mixed Mobility
Emerging mobility systems, e.g., connected and automated vehicles and shared mobility, provide the most intriguing opportunity for enabling users to better monitor transportation network conditions and make better decisions for improving safety and transportation efficiency. However, different levels of vehicle automation in the transportation network can significantly alter transportation efficiency metrics (travel times, energy, environmental […]
Lighting the Way Forward for Autonomous Vehicles
CISE Faculty Affiliate Ajay Joshi with collaborators at Lightmatter and Harvard University receive $4.8M IARPA grant to develop a new Electro-Photonic Computing (EPiC) system for AI-based navigation in Autonomous Vehicles Anyone who has ever been behind the wheel of a car knows that response time is crucial. The human sensory system needs to be fully engaged in order […]
Electro-Photonic Computing (EPiC) for On-Premise Applications
Researchers from Boston University College of Engineering, the Harvard John A. Paulson School of Engineering and Applied Sciences, and photonic-computing developer Lightmatter are collaborating to develop an Electro-Photonic Computing (EPiC) solution for Autonomous Vehicles (AVs), solving one of the biggest hurdles AVs face today – delivering high performance, low latency computing power that is also […]
SHF: Small: Architecting the COSMOS:A Combined System of Optical Phase Change Memory and Optical Links
Today’s data-intensive applications that use graph processing, machine learning or privacy-preserving paradigms demand memory sizes on the order of hundreds of GigaBytes and bandwidths on the order of TeraBytes per second. To support the ever-growing memory needs of the applications, Dynamic Random Access Memory (DRAM) systems have evolved over the decades. However, DRAM will not […]
Cars that learn how to drive themselves by watching other cars
Self-driving cars are powered by machine learning algorithms that require vast amounts of driving data in order to function safely. But if self-driving cars could learn to drive in the same way that babies learn to walk—by watching and mimicking others around them—they would require far less compiled driving data. That idea is pushing Boston […]
Super Headlights: Superconducting Nanowire Detectors for Passive Infrared Sensing
CISE Faculty Affiliate Professor Vivek Goyal (ECE) received a Defense Advanced Research Projects Agency (DARPA) subaward for his work in connection with the agency’s Invisible Headlights program. Professor Goyal is working under an award to MIT entitled, “Super Headlights: Superconducting Nanowire Detectors for Passive Infrared Sensing.” The DARPA Invisible Headlights program has a very ambitious […]
Accessible autonomous vehicle system wins semifinalist position in DOT competition
As the promise of self-driving cars inches closer to reality for the general public, how do we ensure this technology is accessible to people who are blind or visually impaired? That’s the question that ECE Assistant Professor Eshed Ohn-Bar and his team seek to answer with their OpenGuide project. The project has been selected as a semifinalist […]
New Frontiers in Self-driving Cars
Lidar, used in most self-driving cars, models the world around them by creating 3D representations of a scene in view. Photo by John D. SL/Shutterstock One of the most promising developments born out of the 2005 DARPA Grand Challenge was the use of lidar technology in self-driving cars, also known as autonomous vehicles (AVs). […]
Collaborative Research: CIF: Small: Toward a Science of Vehicular Epidemiology: Computing, Control, and Security
Vehicle-to-everything communication is a transformational emerging technology that is poised to enhance transportation safety and traffic management, thereby saving lives and yielding major economic gains by alleviating traffic congestion. This technology will enable vehicles to communicate with each other, as well as with traffic signals, road side units, bikes, wheelchairs, and pedestrians to augment situational […]
Changing the Ways of Selfish Driving
Salomón Wollenstein-Betech (PhD candidate, SE) develops dynamic, data-driven tools to estimate and optimize congested roadways Boston has the worst traffic in the nation, according to a new study released this week. As city traffic thickens, the quintessential need for a resolution to this problem arises. Boston University PhD candidate (Systems Engineering) Salomón Wollenstein-Betech has created a […]