Junior: The Stanford Entry in the Urban Challenge

Montemerlo, Michael; Becker, Jan; Bhat, Suhrid; Dahlkamp, Hendrik; Dolgov, Dmitri; Ettinger, Scott; Hähnel, Dirk; Hilden, Tim; Hoffmann, Gabe; Huhnke, Burkhard; Johnston, Doug; Klumpp, Stefan; Langer, Dirk; Levandowski, Anthony; Levinson, Jesse; Marcil, Julien; Orenstein, David; Paefgen, Johannes; Penny, Isaac; Petrovskaya, Anna; Pflueger, Mike; Stanek, Ganymed; Stavens, David; Vogt, Antone; Thrun, Sebastian

doi:10.1007/978-3-642-03991-1_3

Cited by 187 publications

(210 citation statements)

References 3 publications

Supporting

Mentioning

205

Contrasting

Unclassified

Order By: Relevance

“…High powered laser scanners achieve longer ranges and can provide detailed range/intensity images (e.g., the picture-like intensity images of street roads in Montemerlo et al [33]). For lowpowered eye-safe laser scanners (like the URG-04LX range scanner) the intensity value decays quickly with range (as reported by Kawata et al [36]).…”

Section: Laser Intensity For People Detectionmentioning

confidence: 99%

“…Nüchter et al [31,32] uses range and intensity data, together with Haar-like features for object classification. In Montemerlo et al [33] laser intensity is used for extracting road lanes from an autonomous vehicle. Ye [34] uses laser range and intensity to detect and remove mixed pixels: spurious range measurements between two different surfaces.…”

Section: Laser Intensity For People Detectionmentioning

confidence: 99%

See 1 more Smart Citation

Reliable People Detection Using Range and Intensity Data from Multiple Layers of Laser Range Finders on a Mobile Robot

Carballo

Ohya

Yuta

2011

Int J of Soc Robotics

View full text Add to dashboard Cite

Reliable people detection is an important task in several areas like security, intelligent environments and human robot interaction. People detection does not depend only upon separation of static environment objects from those showing motion (hopefully humans), a reliable system should be able to detect static people even in cluttered environments.This work presents a reliable approach for people detection and position estimation using multiple layers of Laser Range Finders (LRF) on a mobile robot. Each layer combines two LRF sensors to scan around the robot's surroundings and are vertically separated to detect distinct parts of the human body. By using AdaBoost we create strong classifiers to detect body parts, candidate segments in each layer are fused for people detection, and we use simple data association to estimate their positions. Additionally, this work introduces laser reflection intensity as a novel property for people detection. First, we present a study of laser intensity and textiles, then introduce new intensity-based features for detection, and propose a method for segment separation using laser intensity. We provide a thorough evaluation of our multi-layered system though several experiments on a mobile robot.

show abstract

Section: Laser Intensity For People Detectionmentioning

confidence: 99%

Section: Laser Intensity For People Detectionmentioning

confidence: 99%

Reliable People Detection Using Range and Intensity Data from Multiple Layers of Laser Range Finders on a Mobile Robot

Carballo

Ohya

Yuta

2011

Int J of Soc Robotics

View full text Add to dashboard Cite

show abstract

“…These technologies will be gradually developed from semi-autonomous driving to full autonomous driving for driving safety and comfort. The progress of autonomous vehicle technologies requires the ability to handle realistic road scenarios including exceptional driving situations (Bishop, 2005;Montemerlo et al, 2008;Kuwata et al, 2009;Levinson et al, 2011;Urmson et al, 2008). These needs require path planning for autonomous vehicles in urban driving scenarios for both high speed navigation for structured roads and complex navigation for unstructured roads.…”

Section: Introductionmentioning

confidence: 98%

“…If the discretized state space has more dimensions to improve the quality of the solution, the search algorithm requires exponential growth of the memory usage and computational load due to the incremental nature of the algorithm. In the second phase, therefore, a path planner improves the quality of the primitive path performing numerical optimization (Urmson et al, 2008;Pivtoraiko et al, 2009;Pivtoraiko and Kelly, 2005;Montemerlo et al, 2008;Ferguson et al, 2008;Likhachev and Ferguson, 2009;Dolgov and Thrun, 2009). The main challenges for numerical optimization are to compute collision-free paths guaranteeing numerical convergence and efficiency in a complex environment.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Real-time path planning of autonomous vehicles for unstructured road navigation

Chu

Kim

et al. 2015

Int.J Automot. Technol.

View full text Add to dashboard Cite

This paper presents a path planning algorithm for autonomous vehicles to generate feasible and smooth maneuvers in unstructured environments. Our approach uses a graph-based search algorithm and a discrete kinematic vehicle model to find a primitive path that is non-holonomic, collision-free, and safe. The algorithm then improves the quality and smoothness of the identified path by making local shortcuts. This shortcut for smoothing the path is determined by connecting the internal state of the primitive path to the initial state based on the Pythagorean Hodograph (PH) cubic curve. The formulation of the PH cubic curves in the smoothing path provides arc-length parameterization of the curve in a closed form.We present examples and experimental results for the real time path planning in unstructured road from an implementation on an autonomous vehicle.

show abstract

Optimization‐based autonomous racing of 1:43 scale RC cars

Liniger

Domahidi

Morari

2014

Optim Control Appl Methods

408

360

View full text Add to dashboard Cite

This paper describes autonomous racing of RC race cars based on mathematical optimization. Using a dynamical model of the vehicle, control inputs are computed by receding horizon based controllers, where the objective is to maximize progress on the track subject to the requirement of staying on the track and avoiding opponents. Two different control formulations are presented. The first controller employs a two-level structure, consisting of a path planner and a nonlinear model predictive controller (NMPC) for tracking. The second controller combines both tasks in one nonlinear optimization problem (NLP) following the ideas of contouring control. Linear time varying models obtained by linearization are used to build local approximations of the control NLPs in the form of convex quadratic programs (QPs) at each sampling time. The resulting QPs have a typical MPC structure and can be solved in the range of milliseconds by recent structure exploiting solvers, which is key to the real-time feasibility of the overall control scheme. Obstacle avoidance is incorporated by means of a high-level corridor planner based on dynamic programming, which generates convex constraints for the controllers according to the current position of opponents and the track layout. The control performance is investigated experimentally using 1:43 scale RC race cars, driven at speeds of more than 3 m/s and in operating regions with saturated rear tire forces (drifting). The algorithms run at 50 Hz sampling rate on embedded computing platforms, demonstrating the real-time feasibility and high performance of optimization-based approaches for autonomous racing. I. INTRODUCTIONAutonomous car racing is a challenging task for automatic control systems due to the need for handling the vehicle close to its stability limits and in highly nonlinear operating regimes. In addition, dynamically changing racing situations require advanced path planning mechanisms with obstacle avoidance executed in real-time. Fast dynamics constrain the sampling time to be in the range of a few tens of milliseconds at most, which severely limits the admissible computational complexity of the algorithms. This situation is even more challenging if the autonomous algorithms shall be executed on simple, low-power embedded computing platforms.In this paper, we investigate optimization-based control strategies for the task of racing an autonomous vehicle around a given track. We focus on methods that can be implemented to run in real-time on embedded control platforms, and present experimental results using 1:43 scale Kyosho dnano RC race cars that achieve top speeds of more than 3 m/s, which corresponds to an upscaled speed of about 465 km/h. For high performance, the proposed controllers operate the car at its friction limits, far beyond the linear region of what is typically used in other autonomous driving systems. This challenging task is generally mastered only by expert drivers with lots of training. In contrast, our approach requires merely a map of the track a...

show abstract

Junior: The Stanford Entry in the Urban Challenge

Cited by 187 publications

References 3 publications

Reliable People Detection Using Range and Intensity Data from Multiple Layers of Laser Range Finders on a Mobile Robot

Reliable People Detection Using Range and Intensity Data from Multiple Layers of Laser Range Finders on a Mobile Robot

Real-time path planning of autonomous vehicles for unstructured road navigation

Optimization‐based autonomous racing of 1:43 scale RC cars

Contact Info

Product

Resources

About