Finding multiple lanes in urban road networks with vision and lidar
By Albert S. Huang, David C. Moore, Matthew E. Antone, Edwin Olson, Seth J. Teller. 2009
116 citations recorded by the source corpus.
This paper describes a system for detecting and estimating the properties of multiple travel lanes in an urban road network from calibrated video imagery and laser range data acquired by a moving vehicle. The system operates in real-time in several stages on multiple processors, fusing detected road markings, obstacles, and curbs into a stable non-parametric estimate of nearby travel lanes. The system incorporates elements of a provided piecewise-linear road network as a weak prior.Our method is notable in several respects: it detects and estimates multiple travel lanes; it fuses asynchronous, heterogeneous sensor streams; it handles high-curvature roads; and it makes no assumption about the position or orientation of the vehicle with respect to the road.We analyze the system’s performance in the context of the 2007 DARPA Urban Challenge. With five cameras and thirteen lidars, our method was incorporated into a closed-loop controller to successfully guide an autonomous vehicle through a 90 km urban course at speeds up to 40 km/h amidst moving traffic.
Subfields: Automotive Engineering, Computer Vision and Pattern Recognition, Environmental Engineering
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Automotive Engineering
In Computer Vision and Pattern Recognition
- Ekim Yurtsever
- Alexander Carballo
- Jacob Lambert
- Kazuya Takeda
- Ren‐Hao Chen
- Long Chen
- Wenbo Chu
- Yaodong Cui
In Environmental Engineering
Citations in the corpus
- A Survey of Autonomous Driving: Common Practices and Emerging Technologies (2019, positive): “Stereo camera systems [204], as well as 3D lidars [201], have been used determine the 3D structure of roads directly.”
- Find your own way: Weakly-supervised segmentation of path proposals for urban autonomy (2016, unknown): “The survey vehicle (left) is equipped with a camera C and obstacle sensor L , e.g. a LIDAR scanner.”
- Deep Learning for Image and Point Cloud Fusion in Autonomous Driving: A Review (2020, methodology): “These methods [96] [97] [98] [99] mainly rely on vision for road/lane detection while utilizing LiDAR for the curb fitting”
- Recent progress in road and lane detection: a survey (2014, unknown): “The accuracy of these measurements is limited, and sometimes an IMU is used for these purposes instead [2].”
- Overview of Environment Perception for Intelligent Vehicles (2017, unknown): “Compared with the steerable filter, a method with fixed vertical and horizontal kernels was proposed with the advantage of fast execution and disadvantage of low sensitivity to certain line orientations [24].”