Pedestrian Detection with YOLOv5 in Autonomous Driving Scenario

Abstract Inverse reinforcement learning (IRL) has been successfully applied in many robotics and autonomous driving studies without the need for hand-tuning a reward function. However, it suffers from safety issues. Compared to the reinforcement learning (RL) algorithms, IRL is even more vulnerable to unsafe situations as it can only infer the importance of safety based on expert demonstrations. In this paper, we propose a safety-aware adversarial inverse reinforcement learning algorithm (S-AIRL). First, the control barrier function (CBF) is used to guide the training of a safety critic, which leverages the knowledge of system dynamics in the sampling process without training an additional guiding policy. The trained safety critic is then integrated into the discriminator to help discern the generated data and expert demonstrations from the standpoint of safety. Finally, to further improve the safety awareness, a regulator is introduced in the loss function of the discriminator training to prevent the recovered reward function from assigning high rewards to the risky behaviors. We tested our S-AIRL in the highway autonomous driving scenario. Comparing to the original AIRL algorithm, with the same level of imitation learning (IL) performance, the proposed S-AIRL can reduce the collision rate by 32.6%.

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Fusing Appearance and Prior Cues for Road Detection

Applied Sciences ◽

10.3390/app9050996 ◽

2019 ◽

Vol 9 (5) ◽

pp. 996

Author(s):

Fenglei Ren ◽

Xin He ◽

Zhonghui Wei ◽

Lei Zhang ◽

Jiawei He ◽

...

Keyword(s):

Pedestrian Detection ◽

Autonomous Driving ◽

Relevant Information ◽

Gaussian Mixture ◽

Morphological Processing ◽

Road Detection ◽

Collision Warning ◽

The Road ◽

Illuminant Invariant ◽

Institute Of Technology

Road detection is a crucial research topic in computer vision, especially in the framework of autonomous driving and driver assistance. Moreover, it is an invaluable step for other tasks such as collision warning, vehicle detection, and pedestrian detection. Nevertheless, road detection remains challenging due to the presence of continuously changing backgrounds, varying illumination (shadows and highlights), variability of road appearance (size, shape, and color), and differently shaped objects (lane markings, vehicles, and pedestrians). In this paper, we propose an algorithm fusing appearance and prior cues for road detection. Firstly, input images are preprocessed by simple linear iterative clustering (SLIC), morphological processing, and illuminant invariant transformation to get superpixels and remove lane markings, shadows, and highlights. Then, we design a novel seed superpixels selection method and model appearance cues using the Gaussian mixture model with the selected seed superpixels. Next, we propose to construct a road geometric prior model offline, which can provide statistical descriptions and relevant information to infer the location of the road surface. Finally, a Bayesian framework is used to fuse appearance and prior cues. Experiments are carried out on the Karlsruhe Institute of Technology and Toyota Technological Institute (KITTI) road benchmark where the proposed algorithm shows compelling performance and achieves state-of-the-art results among the model-based methods.

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Pedestrian detection and recognition using lidar for autonomous driving

2019 International Conference on Optical Instruments and Technology: Optical Sensors and Applications ◽

10.1117/12.2550057 ◽

2020 ◽

Author(s):

Jiao Mao ◽

Guoliang Xu ◽

Wanlin Li ◽

Xiaohui Fan ◽

Jiangtao Luo

Keyword(s):

Pedestrian Detection ◽

Autonomous Driving ◽

Detection And Recognition

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GeoScenario: An Open DSL for Autonomous Driving Scenario Representation

2019 IEEE Intelligent Vehicles Symposium (IV) ◽

10.1109/ivs.2019.8814107 ◽

2019 ◽

Cited By ~ 4

Author(s):

Rodrigo Queiroz ◽

Thorsten Berger ◽

Krzysztof Czarnecki

Keyword(s):

Autonomous Driving ◽

Driving Scenario

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SoundTrAD, A Method and Tool for Prototyping Auditory Displays: Can We Apply It to an Autonomous Driving Scenario?

Proceedings of the 24th International Conference on Auditory Display - ICAD 2018 ◽

10.21785/icad2018.009 ◽

2018 ◽

Author(s):

Doon MacDonald ◽

Tony Stockman

Keyword(s):

User Interface ◽

Interface Design ◽

Autonomous Driving ◽

User Interface Design ◽

Auditory Display ◽

Auditory Displays ◽

Fundamental Structure ◽

Wide Range ◽

Driving Scenario

This paper presents SoundTrAD, a method and tool for designing auditory displays for the user interface. SoundTrAD brings together ideas from user interface design and soundtrack composition and supports novice auditory display designers in building an auditory user interface. The paper argues for the need for such a method before going on to describe the fundamental structure of the method and construction of the supporting tools. The second half of the paper applies SoundTrAD to an autonomous driving scenario and demonstrates its use in prototyping ADs for a wide range of scenarios.

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Unsupervised Domain Adaptation in Semantic Segmentation: A Review

Technologies ◽

10.3390/technologies8020035 ◽

2020 ◽

Vol 8 (2) ◽

pp. 35

Author(s):

Marco Toldo ◽

Andrea Maracani ◽

Umberto Michieli ◽

Pietro Zanuttigh

Keyword(s):

Ad Hoc ◽

Domain Adaptation ◽

Semantic Segmentation ◽

Autonomous Driving ◽

Input Image ◽

Comprehensive Overview ◽

Open Problems ◽

Unsupervised Domain Adaptation ◽

Driving Scenario ◽

Segmentation Models

The aim of this paper is to give an overview of the recent advancements in the Unsupervised Domain Adaptation (UDA) of deep networks for semantic segmentation. This task is attracting a wide interest since semantic segmentation models require a huge amount of labeled data and the lack of data fitting specific requirements is the main limitation in the deployment of these techniques. This field has been recently explored and has rapidly grown with a large number of ad-hoc approaches. This motivates us to build a comprehensive overview of the proposed methodologies and to provide a clear categorization. In this paper, we start by introducing the problem, its formulation and the various scenarios that can be considered. Then, we introduce the different levels at which adaptation strategies may be applied: namely, at the input (image) level, at the internal features representation and at the output level. Furthermore, we present a detailed overview of the literature in the field, dividing previous methods based on the following (non mutually exclusive) categories: adversarial learning, generative-based, analysis of the classifier discrepancies, self-teaching, entropy minimization, curriculum learning and multi-task learning. Novel research directions are also briefly introduced to give a hint of interesting open problems in the field. Finally, a comparison of the performance of the various methods in the widely used autonomous driving scenario is presented.

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