search

UMD     This Site






University of Maryland research into using machine learning to predict human driver behavior was featured in Tech Explore. The article, “B-GAP: A simulation method for training autonomous vehicles to navigate complex urban scenes,” highlighted work that was recently published in IEEE Robotics and Automation Letters.

The researchers, headed by Distinguished University Professor Dinesh Manocha (Department of Electrical and Computer Engineering and Department of Computer Science), include computer science Ph.D. students Angelos Mavrogiannis and Rohan Chandra.

The team recently developed a new technique that could improve the effectiveness of current simulators used to train models for self-driving vehicle navigation. Their technique builds previous research focusing on autonomous vehicle navigation and behavior classification.

"While there is currently a lot of interest in autonomous navigation for self-driving cars, current AI methods used for navigation do not take into account the behavior of human drivers or other autonomous vehicles on the road," Manocha told TechXplore. "The goals of our work are to develop robust technologies that can detect and classify the behaviors of other road agents (e.g., vehicles, buses, trucks, bicycles, pedestrians) and use these behaviors to guide the driving trajectories of autonomous vehicles."

Beyond the complexities of autonomous navigation in its own right, are the additional complexities of autonomous navigation in a dense and constantly changing environment such as high-population, urban environments. Furthermore, it is a major challenge to develop safe autonomous driving technologies that can adapt to the driving patterns or behaviors of human drivers.

As part of their work, the team classified human-driver behaviors based on conservative versus aggressive metric, and then using novel computational methods, the team developed a model, dubbed CMetric which analyzes the behaviors of other drivers in dense, mixed traffic scenarios.

With the addition of more vehicles—and more importantly, more drivers with potentially unpredictable behavior—the need for more robust models for the future success of autonomous vehicles in these environments is critical.

Additional information on the team’s work can be found here.

Angelos Mavrogiannis, Rohan Chandra, Dinesh Manocha, "B-GAP: Behavior-Rich Simulation and Navigation for Autonomous Driving", Robotics and Automation Letters IEEE, vol. 7, no. 2, pp. 4718-4725, 2022.



Related Articles:
MRC and MAGE Earn ARM Institute Endorsement
Helping robots navigate to a target, around obstacles and without a map
UMD Dedicates IDEA Factory
UMD Researchers Eye Advances in Autonomy
Manocha Receives 2022 Verisk AI Faculty Research Award
Algorithm helps autonomous vehicles navigate common tricky traffic situations
NASA's Perseverance launches to Mars; alumnus work aboard
Discoveries from NASA's Parker Solar Probe published in Nature
Advancing Healthcare through Robotics and Machine Learning
Special Delivery

April 13, 2022


«Previous Story  

 

 

Current Headlines

ECE/ISR Ph.D. Students Win Hardware Demo Competitions in HOST Symposium  

Two Papers on  Gut-Microbiome-Brain-Axis (GMBA) Published

University of Maryland Has Strong Presence at ICRA 2024

Khaligh Honored With Linda Clement Outstanding Advisor Award

UMD Launches Institute Focused on Ethical AI Development

Remembering Rance Cleaveland (1961-2024)

Dinesh Manocha Inducted into IEEE VGTC Virtual Reality Academy

ECE Ph.D. Student Ayooluwa (“Ayo”) Ajiboye Recognized at APEC 2024

Balachandran, Cameron, Yu Receive 2024 MURI Award

UMD, Booz Allen Hamilton Announce Collaboration with MMEC

 
 
Back to top  
Home Clark School Home UMD Home