Autonomous vehicles control in the VisLab Intercontinental Autonomous Challenge

Broggi, Alberto; Medici, Paolo; Zani, Paolo; Coati, Alessandro; Panciroli, Matteo

doi:10.1016/j.arcontrol.2012.03.012

Cited by 105 publications

(49 citation statements)

References 14 publications

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“…In this paper, we use polyfit function from Matlab to smooth the lateral and longitudinal acceleration data of taxi driver as depicted in , for each motion set , using (4). Before evaluation, we validate generated lateral and longitudinal motion using maximum curvature formula from [17]. We remove the motions that exceeds the maximum curvature to avoid slip or fishtailing.…”

Section: ( ) ( ) ∈mentioning

confidence: 99%

Evaluating human & computer for expressway lane changing

Tehrani

Muto

Yoneda³

et al. 2014

2014 IEEE Intelligent Vehicles Symposium Proceedings

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Google already showed the self-driving car can do stopping and accelerating more smoothly than the human drivers by doing extensive experiments. In this paper, we compare a human driver with computer generated motion for expressway lane changing. We did experiments at Nagoya ring route expressway and recorded all the detail motion information regarding lateral and longitudinal movement of a taxi driver. We developed an algorithm to generate and evaluate the alternative lane change motion sets considering both lateral and longitudinal trajectories. We defined a smoothness measure that integrates lateral and longitudinal motions to evaluate generated lane change motions. Evaluation results for 19 sets of lane change labeled data show the computer motion generator is able to generate close or better motion compared with a taxi driver using proposed evaluation function.

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Section: ( ) ( ) ∈mentioning

confidence: 99%

Evaluating human & computer for expressway lane changing

Tehrani

Muto

Yoneda³

et al. 2014

2014 IEEE Intelligent Vehicles Symposium Proceedings

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“…The path planner is based on several concepts developed for VIAC [16]. Since both steering and acceleration are controlled by the planner, this system includes obstacle avoidance and collision mitigation functions.…”

Section: B Path Plannermentioning

confidence: 99%

PROUD-Public road urban driverless test: Architecture and results

Broggi

Cerri

Debattisti

et al. 2014

2014 IEEE Intelligent Vehicles Symposium Proceedings

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Abstract-The presence of autonomous vehicles on public roads is becoming a reality. In the last 10 years, autonomous prototypes have been confined in controlled or isolated environments, but new traffic regulations for testing and direct automotive companies interests are moving autonomous vehicles tests on real roads. This paper presents a test on public urban roads and freeways that was held in Parma on July 12, 2013. This was the first test in open public urban roads with nobody behind the steering wheel: the vehicle had to cope with roundabouts, junctions, pedestrian crossings, freeway junctions, traffic lights, and regular traffic. The vehicle setup, the software architecture, and the route are here presented together with some results and possible future improvements.

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“…Motivated by this success, several efforts have been conducted towards autonomous driving in real traffic [10], [29], [51] triggering research on lane estimation [3], 1. http://www.cvlibs.net/projects/intersection [18], [21], [41], [49] and road detection [1], [2], [17] algorithms.…”

Section: Related Workmentioning

confidence: 99%

3D Traffic Scene Understanding From Movable Platforms

Geiger

Lauer

Wojek

et al. 2014

IEEE Trans. Pattern Anal. Mach. Intell.

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Abstract-In this paper, we present a novel probabilistic generative model for multi-object traffic scene understanding from movable platforms which reasons jointly about the 3D scene layout as well as the location and orientation of objects in the scene. In particular, the scene topology, geometry and traffic activities are inferred from short video sequences. Inspired by the impressive driving capabilities of humans, our model does not rely on GPS, lidar or map knowledge. Instead, it takes advantage of a diverse set of visual cues in the form of vehicle tracklets, vanishing points, semantic scene labels, scene flow and occupancy grids. For each of these cues we propose likelihood functions that are integrated into a probabilistic generative model. We learn all model parameters from training data using contrastive divergence. Experiments conducted on videos of 113 representative intersections show that our approach successfully infers the correct layout in a variety of very challenging scenarios. To evaluate the importance of each feature cue, experiments using different feature combinations are conducted. Furthermore, we show how by employing context derived from the proposed method we are able to improve over the state-of-the-art in terms of object detection and object orientation estimation in challenging and cluttered urban environments.

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Autonomous vehicles control in the VisLab Intercontinental Autonomous Challenge

Cited by 105 publications

References 14 publications

Evaluating human & computer for expressway lane changing

Evaluating human & computer for expressway lane changing

PROUD-Public road urban driverless test: Architecture and results

3D Traffic Scene Understanding From Movable Platforms

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Autonomous vehicles control in the VisLab Intercontinental Autonomous Challenge

Cited by 105 publications

References 14 publications

Evaluating human &amp; computer for expressway lane changing

Evaluating human &amp; computer for expressway lane changing

PROUD-Public road urban driverless test: Architecture and results

3D Traffic Scene Understanding From Movable Platforms

Contact Info

Product

Resources

About

Evaluating human & computer for expressway lane changing

Evaluating human & computer for expressway lane changing