Linda Angell
Papers
2
Total Citations
304
H-Index
2
About
Linda Angell is a leading researcher in human factors and autonomous vehicle safety, whose work centers on understanding how drivers interact with vehicle automation. Her major contributions stem from the MIT Advanced Vehicle Technology Study, where she leads large-scale naturalistic driving studies that capture real-world driver behavior. Her most-cited paper (2019, 227 citations) argues that the full driving task remains too complex to be fully formalized as a sensing-acting robotics system, highlighting the limitations of current model-based and learning-based approaches for achieving unconstrained autonomy. This work, along with her 2017 study (77 citations), uses deep learning to analyze driver behavior and interaction with automation, providing critical insights into the challenges of human-machine collaboration. Angell’s research has profound implications for the design of safer autonomous systems, emphasizing that human factors must be central to technological development. Her studies are foundational for engineers and policymakers working on vehicle automation, and she is widely recognized for bridging the gap between theoretical autonomy and practical, human-centered safety.
Research Focus
Key Achievements
Top Papers
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