A perception‐driven autonomous urban vehicle

By John J. Leonard, Jonathan P. How, Seth J. Teller, Mitch Berger, S. Campbell, Gaston A. Fiore, L. Fletcher, Emilio Frazzoli, Albert S. Huang, Sertac Karaman and 18 more. 2008

586 citations recorded by the source corpus.

Subfields: Aerospace Engineering, Automotive Engineering, Computer Vision and Pattern Recognition

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Citations in the corpus

A Survey of Autonomous Driving: Common Practices and Emerging Technologies (2019, methodology): “While not as accurate as the lidar, it can detect at object at high range and estimate their velocity [112].”
A Survey of Motion Planning and Control Techniques for Self-Driving Urban Vehicles (2016, methodology): “Finally, the vehicle developed by MIT used a variant of RRT algorithm called closed-loop RRT with biased sampling [124].”
Recent Advancements in End-to-End Autonomous Driving Using Deep Learning: A Survey (2023, background): “It often involves projecting geometric features from various perception sensors onto image space, such as bird’s-eye view [28], [38] [22], [36], [45] and range view [39], [46], [47].”
Model based vehicle detection and tracking for autonomous urban driving (2009, methodology): “Several successful approaches have been developed in parallel by the UGC participants (Leonard et al. 2008; Urmson et al. 2008).”
Constraint-based planning and control for safe, semi-autonomous operation of vehicles (2012, methodology): “Common path planning tools include rapidly-exploring random trees [7], graph search methods [8], potential fields analysis [9], and neural optimization techniques [10].”
GPT-Driver: Learning to Drive with GPT (2023, methodology): “The rule-based methods Treiber et al. (2000); Thrun et al. (2006); Bacha et al. (2008); Leonard et al. (2008); Urmson et al. (2008); Chen et al. (2015); Sauer et al. (2018); Fan et al. (2018) designed explicit rules to determine driving trajectories.”
Autonomous driving in urban environments: approaches, lessons and challenges (2010, background): “New techniques in hybrid systems (Frehse 2008), model checking (Lamport 2002; Holzmann 2003) and stability certificates (Parrilo 2003) provide promising directions for future research.”
Safe driving envelopes for path tracking in autonomous vehicles (2017, unknown): “In an autonomous vehicle, it can be useful to divide motion control into path planning and path tracking [1][2][3].”
Model-free detection and tracking of dynamic objects with 2D lidar (2015, methodology): “To take advantage of the multilayered scanner, we follow a standard procedure to remove ground strike measurements given multiple scanning layers described by Leonard et al. (2008). Specifically, a grid is first built over the 2D scanning plane, measurements from each scanning layer are projected do”
Planning and Decision-Making for Autonomous Vehicles (2018, background): “The 2004–2007 Defense Advanced Research Projects Agency (DARPA)–sponsored competitions (8, 9) pushed research on automated driving to near-real-world conditions (10, 11).”
An Invitation to Imitation (2015, background): “This architecture has been replicated and reﬁned throughout the ﬁeld of robotics [Zucker et al., 2011 , Urmson et al., 2008 , Welling-ton and Stentz, 2004 , Leonard et al., 2008 , Jackel et al., 2006 , Bachrach et al., 2009 ] and is currently used in the most advanced autonomous navigation systems.”
Information-Theoretic Model Predictive Control: Theory and Applications to Autonomous Driving (2017, background): “Although the hierarchical approach makes the control problem tractable, and has many successful applications to autonomous vehicles [3]–[7], the decomposition into planning and execution phases introduces inherent limitations.”
Real-Time Perception Meets Reactive Motion Generation (2017, background): “Combining local control with motion planning is quite common in the area of mobile robots, e.g., [1], [2], [18], [19].”