Visualizing the Learning Progress of Self-Driving Cars

Mund, Sandro; Frank, Raphaël; Varisteas, Georgios; State, Radu

doi:10.1109/itsc.2018.8569464

Cited by 4 publications

(7 citation statements)

References 5 publications

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“…2) Evaluation and Limitations: The driving performance of self-driving algorithms is evaluated in simulated environments [229], [230], [248], using pre-recorded datasets [220], [224], [247], [249], or both [222]. In simulations, safety metrics measure the number of collisions or infractions and the distance traveled between them [222], [229], whereas on pre-recorded data, metrics assess how well the algorithm matches vehicle data, e.g.…”

Section: B Attention For Self-driving Vehiclesmentioning

confidence: 99%

Attention for Vision-Based Assistive and Automated Driving: A Review of Algorithms and Datasets

Kotseruba

Tsotsos

2022

IEEE Trans. Intell. Transport. Syst.

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Driving safety has been a concern since the first cars appeared on the streets. Driver inattention has been singled out as a major cause of accidents early on. This is hardly surprising, as drivers routinely perform other tasks in addition to controlling the vehicle. Decades of research into what causes lapses or misdirection of drivers' attention resulted in improvements in road safety through better design of infrastructure, driver training programs, in-vehicle interfaces, and, more recently, the development of driving assistance systems (ADAS) and driving automation. This review focuses on the methods for modeling and detecting spatio-temporal aspects of drivers' attention, i.e. where and when they look, for the two latter categories of applications.We start with a brief theoretical background on human visual attention, methods for recording and measuring attention in the driving context, types of driver inattention, and factors causing it. We then discuss machine learning approaches for 1) modeling gaze for assistive and self-driving applications and 2) detecting gaze for driver monitoring. Following the overview of state-of-the-art models, we provide an extensive list of publicly available datasets that feature recordings of drivers' gaze and other attention-related annotations. We conclude with a general overview of the remaining challenges, such as data availability and quality, evaluation methods, and the limited scope of attention modeling, and outline steps toward rectifying some of these issues. Categorized and annotated lists of the reviewed models and datasets are available at https://github.com/ykotseruba/attention_and_driving

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Section: B Attention For Self-driving Vehiclesmentioning

confidence: 99%

Attention for Vision-Based Assistive and Automated Driving: A Review of Algorithms and Datasets

Kotseruba

Tsotsos

2022

IEEE Trans. Intell. Transport. Syst.

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“…Various models as described in (Smolyakov,Frolov [39], Woo, Yu [40], Wang, Wen [41], Sharma, Tewolde [42] & Simmons, Adwani [43]) predict continuous steering commands from raw input pixels applying various approaches; some achieve this through an end-to-end method. Even though these models offer a high level of accuracy, what happens on the various layers of the network remains unknown, which makes it a prerequisite for the car manufacturing companies and their first-tier suppliers to apprehend and lawfully verify that these approaches yield the correct output before they can be adopted for commercialized AVs [44]. Picturing what the model perceives on various layers is crucial in order to develop improved networks and avoid a trial and error approach.…”

Section: Environment St St+1mentioning

confidence: 99%

“…where is a steering control command that defines the RPM commands of the main thrusters as in (44). Since the goal of the path-following problem is to minimize the crosstrack error and the course angle error without producing chattering, partial reward functions are defined as 46…”

Section: Environment St St+1mentioning

confidence: 99%

“…However, overfitting issues, CPU thermal throttling degraded the performance in the case of processing multiple DNN models in the network simultaneously, and it had high energy consumption. [44] proposed CNN with a generic visualization model that utilized attention heatmaps (AHs) by stressing image regions that were most relevant for steering control. This was to predict longitudinal and lateral control that involved steering, acceleration, and braking.…”

Section: A Convolutional Neural Network In Autonomous Vehicle Steerimentioning

confidence: 99%

“…[80] Smooth track with curves of 10 minutes at 50% throttle. [44] Contains divers' situations, marked with respective turning angles of steering wheel of up to 38k sequential frames; traffic lights were removed [81] More than 10,000km and 200 hours of driving on open road with various kinds of urban streets, highways and country roads; 2000 frames × 350 episodes and 50% of weather types in town used in the training environment; two-lane dual carriage way [44] Keras/Tensorflow -GPU (Nvidia Tesla K80) [87] Pytorch -GPU (NVIDIA GeForce GTX Titan X…”

Section: Driving Scenarios For Autonomous Vehicles In Simulated Ementioning

confidence: 99%

See 2 more Smart Citations

A Survey on Deep Learning for Steering Angle Prediction in Autonomous Vehicles

et al. 2020

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Steering angle prediction is critical in the control of Autonomous Vehicles (AVs) and has attracted the attention of researchers, manufacturers, and insurance companies in the automotive industry. Different Deep Learning (DL) architectures have been applied to predict the steering angle of AVs in various scenarios. A survey on steering angle prediction based on deep learning algorithms can help expert researchers identify those areas that require development. Also, novice researchers can use the survey as a starting point. In this paper, we present a broad study on the recent advances made in DL architectures that covers the steering angle prediction of AVs. A new comprehensive taxonomy of the application of DL in steering angle prediction of AVs is created. The survey presents a concise research summary synthesis, and analysis. It is found that most researchers depend on Convolutional Neural Network (CNN) over other DL architectures in predicting the steering angle of autonomous driving vehicles. Also identified are open research problems. The prominent challenge facing DL-based steering angle prediction of AVs is lack of sufficient real-world datasets, which means that researchers largely depend on data generated from simulated environments. Lastly, alternative viewpoints to solve the identified open research challenges are proposed, pointing towards promising future research directions.

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Autonomous Driving on the Thin Trail of Great Opportunities and Dangerous Trust

Mund

Glauner

2020

Future of Business and Finance

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Visualizing the Learning Progress of Self-Driving Cars

Cited by 4 publications

References 5 publications

Attention for Vision-Based Assistive and Automated Driving: A Review of Algorithms and Datasets

Attention for Vision-Based Assistive and Automated Driving: A Review of Algorithms and Datasets

A Survey on Deep Learning for Steering Angle Prediction in Autonomous Vehicles

Autonomous Driving on the Thin Trail of Great Opportunities and Dangerous Trust

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