Navigate like a cabbie: probabilistic reasoning from observed context-aware behavior

By Brian D. Ziebart, Andrew L. Maas, Anind K. Dey, J. Bagnell. 2008

327 citations recorded by the source corpus.

Subfields: Transportation, Signal Processing

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Transportation

In Signal Processing

Citations in the corpus

End-to-End Driving Via Conditional Imitation Learning (2017, background): “Imitation learning has been applied to a variety of tasks, including articulated motion [2], [28], [11], autonomous flight [1], [13], [30], modeling navigational behavior [37], [38], off-road driving [22], [32], and road following [4], [6], [27], [36].”
CIRL: Controllable Imitative Reinforcement Learning for Vision-based Self-driving (2018, background): “As a straight-forward idea, imitation learning that learns policies via supervised training on human driving data has been applied to a variety of tasks, including modeling navigational behavior [20], off-road driving [12,2], and road following [9,8,1,18,11].”
Learning to search: structured prediction techniques for imitation learning (2009, methodology): “The authors’ introductory and application papers (Ziebart et al., 2008a,c) demonstrate this algorithm on driver route prediction problems.”
Modeling Purposeful Adaptive Behavior with the Principle of Maximum Causal Entropy (2010, methodology): “• Maximum entropy inverse optimal control (Ziebart et al., 2008b) is the special case of the maximum causal entropy approach applied to settings with deterministic state transition dynamics.”
iBAT: detecting anomalous taxi trajectories from GPS traces (2011, background): “Gathering and analyzing the large-scale GPS traces have provided us a great opportunity to reveal the hidden “facts” about the city dynamics and human behaviors, enabling diverse innovative applications [22, 13, 18, 30, 26, 21, 23, 28, 24].”
T-Drive: Enhancing Driving Directions with Taxi Drivers' Intelligence (2013, unknown): “Papers [20], [21], [23], [24] present some probabilistic-based methods to predict a user’s destination and route based on historical GPS trajectories.”
Prediction of urban human mobility using large-scale taxi traces and its applications (2012, background): “[12] built valuable navigation services by reasoning on driver behavior.”
An Invitation to Imitation (2015, methodology): “[ Syed and Schapire, 2007 ] Conversely, while methods like the Maximum (Causal) Entropy approach of [Ziebart et al., 2008 a] are also designed to achieve the same reward as a teacher, they can also be understood in a dual formulation as maximizing the likelihood of the teacher’s plans under a…”
Learning From Demonstration in the Wild (2018, unknown): “The same idea has been used to imitate basketball teams [26], predict taxi driver behaviour [27], and control complex animations [28].”
A Survey of Inverse Reinforcement Learning: Challenges, Methods and Progress (2018, background): “In this use, applications include destination or route prediction for taxis [13, 14], footstep prediction for planning legged locomotion [15], anticipation of pedestrian interactions [16], energy efficient driving [17], and penetration of a perimeter patrol [18] by learning the patrollers’ preferenc”
Learning driving styles for autonomous vehicles from demonstration (2015, methodology): “More details about the derivation of this learning algorithm can be found in Ziebart et al. [20] and Kretzschmar et al. [10].”
Robot navigation in dense human crowds: Statistical models and experimental studies of human–robot cooperation (2015, methodology): “[119]: pedestrian decision making is first learned from a large trajectory example database, and then the robot navigates in a way that causes the least disruption to the human’s anticipated paths.”