Autonomous Driving: Context and State-of-the-Art

Ibañez‐Guzmán, Javier; Laugier, Christian; Yoder, John-David; Thrun, Sebastian

doi:10.1007/978-0-85729-085-4_50

Cited by 46 publications

(28 citation statements)

References 41 publications

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“…In concurrence with advancements in desktop and mobile computing power, in-vehicle computing has now reached a stage where there is an ever-increasing trend towards the automation of multiple vehicle functions/systems [28]. Because of this trend, a number of limited automation functions are now readily available in modern vehicles [10]. Many new high-end luxury cars now feature the functions and systems pertaining to level 2 automation.…”

Section: Vehicular Automationmentioning

confidence: 98%

What's around the corner?

Beattie

Baillie

Halvey

et al. 2014

Proceedings of the 8th Nordic Conference on Human-Computer Interaction: Fun, Fast, Foundational

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There is currently a distinct lack of design consideration associated with autonomous vehicles and their impact on human factors. Research has yet to consider fully the impact felt by the driver when he/she is no longer in control of the vehicle [12]. We propose that spatialised auditory feedback could be used to enhance driver awareness to the intended actions of autonomous vehicles. We hypothesise that this feedback will provide drivers with an enhanced sense of control. This paper presents a driving simulator study where 5 separate auditory feedback methods are compared during both autonomous and manual driving scenarios. We found that our spatialised auditory presentation method alerted drivers to the intended actions of autonomous vehicles much more than all other methods and they felt significantly more in control during scenarios containing sound vs. no sound. Finally, that overall workload in autonomous vehicle scenarios was lower compared to manual vehicle scenarios.

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Section: Vehicular Automationmentioning

confidence: 98%

What's around the corner?

Beattie

Baillie

Halvey

et al. 2014

Proceedings of the 8th Nordic Conference on Human-Computer Interaction: Fun, Fast, Foundational

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show abstract

“…The different visual-appearance-based algorithms were evaluated in terms of recall rate 4 and precision rate. 5 We also show the results using a different visualization, namely confusion maps.…”

Section: B Evaluation Criteria and Parameters Used For The Experimentsmentioning

confidence: 99%

“…In recent years, numerous research papers have addressed the development of autonomous Unmanned Ground Vehicles (UGV), leading thus to striking new technologies like self-driving cars. These can map and react in highlyuncertain street environments partially using [3]-or completely neglecting-GPS systems [4]. In the next years, a similar bust in the development of small-sized Unmanned Aerial Vehicles (UAV) is expected.…”

Section: Introductionmentioning

confidence: 99%

MAV urban localization from Google street view data

Majdik

Albers-Schoenberg

Scaramuzza

2013

2013 IEEE/RSJ International Conference on Intelligent Robots and Systems

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Abstract-We tackle the problem of globally localizing a camera-equipped micro aerial vehicle flying within urban environments for which a Google Street View image database exists. To avoid the caveats of current image-search algorithms in case of severe viewpoint changes between the query and the database images, we propose to generate virtual views of the scene, which exploit the air-ground geometry of the system. To limit the computational complexity of the algorithm, we rely on a histogram-voting scheme to select the best putative image correspondences. The proposed approach is tested on a 2km image dataset captured with a small quadroctopter flying in the streets of Zurich. The success of our approach shows that our new air-ground matching algorithm can robustly handle extreme changes in viewpoint, illumination, perceptual aliasing, and over-season variations, thus, outperforming conventional visual place-recognition approaches. MULTIMEDIA MATERIALPlease note that this paper is accompanied by a video demonstration available on our webpage along with the dataset used in this work: rpg.ifi.uzh.ch

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“…Through these research activities, the technical feasibility of highly automated vehicles has been demonstrated. However, significant challenges still remain, in terms of reliability, cost, and legislation [9]. For example, reliable perception and decision of the vehicle's environment can be derived from high-quality sensor data, but that can cause cost problems.…”

Section: Introductionmentioning

confidence: 99%

Curvilinear-Coordinate-Based Object and Situation Assessment for Highly Automated Vehicles

Kim

Lim

et al. 2015

IEEE Trans. Intell. Transport. Syst.

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This paper presents a novel curvilinear-coordinatebased approach to improve object and situation assessment performance for highly automated vehicles under various curved road conditions. The approach integrates object information from radars and lane information from a camera with three steps: track-to-track fusion, curvilinear coordinate conversion, and lane assessment. The track-to-track fusion is achieved through a nearest neighbor filter that updates the target state estimation and covariance with the nearest neighbor measurement, and a crosscovariance method that merges the duplicate tracks using error covariance. In order to determine in which lane the fused tracks are located accurately and reliably, the curvilinear coordinate conversion process is performed. The curvilinear coordinates are generated in the form of a cubic Hermite spline lane model from the lane information of the camera. Based on the converted track information and the lane model in the curvilinear coordinates, the probability distribution of the threat levels in each lane is determined though a probabilistic lane association and threat assessment. The developed algorithm is verified and evaluated through experiments using a real-time embedded system.The results show that the proposed curvilinear-coordinate-based approach provides excellent performance of object and situation assessment, in respect of accuracy and computational efficiency, in real-time operation.

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Autonomous Driving: Context and State-of-the-Art

Cited by 46 publications

References 41 publications

What's around the corner?

What's around the corner?

MAV urban localization from Google street view data

Curvilinear-Coordinate-Based Object and Situation Assessment for Highly Automated Vehicles

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