State-of-the-art and trends of autonomous driving technology

2018 ◽  
Author(s):  
You Dingyi ◽  
Wang Haiyan ◽  
Yang Kaiming
Keyword(s):  
State Of The Art ◽  
Autonomous Driving

scholarly journals One Proxy Device Is Enough for Hardware-Aware Neural Architecture Search

2021 ◽  
Vol 5 (3) ◽  
pp. 1-34
Author(s):  
Bingqian Lu ◽  
Jianyi Yang ◽  
Weiwen Jiang ◽  
Yiyu Shi ◽  
Shaolei Ren
Keyword(s):  
State Of The Art ◽  
Autonomous Driving ◽  
Pareto Optimal ◽  
Video Content ◽  
Fast Evaluation ◽  
Video Content Analysis ◽  
Search Spaces ◽  
Neural Architecture ◽  
Real World Applications ◽  
Prohibitive Cost

Convolutional neural networks (CNNs) are used in numerous real-world applications such as vision-based autonomous driving and video content analysis. To run CNN inference on various target devices, hardware-aware neural architecture search (NAS) is crucial. A key requirement of efficient hardware-aware NAS is the fast evaluation of inference latencies in order to rank different architectures. While building a latency predictor for each target device has been commonly used in state of the art, this is a very time-consuming process, lacking scalability in the presence of extremely diverse devices. In this work, we address the scalability challenge by exploiting latency monotonicity --- the architecture latency rankings on different devices are often correlated. When strong latency monotonicity exists, we can re-use architectures searched for one proxy device on new target devices, without losing optimality. In the absence of strong latency monotonicity, we propose an efficient proxy adaptation technique to significantly boost the latency monotonicity. Finally, we validate our approach and conduct experiments with devices of different platforms on multiple mainstream search spaces, including MobileNet-V2, MobileNet-V3, NAS-Bench-201, ProxylessNAS and FBNet. Our results highlight that, by using just one proxy device, we can find almost the same Pareto-optimal architectures as the existing per-device NAS, while avoiding the prohibitive cost of building a latency predictor for each device.

scholarly journals Mosaic Super-resolution via Sequential Feature Pyramid Networks

2019 ◽  
Author(s):  
Mehrdad Shoeiby ◽  
Mohammad Ali Armin ◽  
Sadegh Aliakbarian ◽  
Saeed Anwar ◽  
Lars petersson
Keyword(s):  
State Of The Art ◽  
Super Resolution ◽  
Autonomous Driving ◽  
Single Shot ◽  
Current State ◽  
Wide Range ◽  
Feature Pyramid ◽  
Novel Method ◽  
Convolutional Lstm ◽  
Mosaic Images

<div>Advances in the design of multi-spectral cameras have</div><div>led to great interests in a wide range of applications, from</div><div>astronomy to autonomous driving. However, such cameras</div><div>inherently suffer from a trade-off between the spatial and</div><div>spectral resolution. In this paper, we propose to address</div><div>this limitation by introducing a novel method to carry out</div><div>super-resolution on raw mosaic images, multi-spectral or</div><div>RGB Bayer, captured by modern real-time single-shot mo-</div><div>saic sensors. To this end, we design a deep super-resolution</div><div>architecture that benefits from a sequential feature pyramid</div><div>along the depth of the network. This, in fact, is achieved</div><div>by utilizing a convolutional LSTM (ConvLSTM) to learn the</div><div>inter-dependencies between features at different receptive</div><div>fields. Additionally, by investigating the effect of different</div><div>attention mechanisms in our framework, we show that a</div><div>ConvLSTM inspired module is able to provide superior at-</div><div>tention in our context. Our extensive experiments and anal-</div><div>yses evidence that our approach yields significant super-</div><div>resolution quality, outperforming current state-of-the-art</div><div>mosaic super-resolution methods on both Bayer and multi-</div><div>spectral images. Additionally, to the best of our knowledge,</div><div>our method is the first specialized method to super-resolve</div><div>mosaic images, whether it be multi-spectral or Bayer.</div><div><br></div>

scholarly journals Deep Learning-Based Monocular Depth Estimation Methods—A State-of-the-Art Review

Sensors ◽  
2020 ◽  
Vol 20 (8) ◽  
pp. 2272 ◽  
Author(s):  
Faisal Khan ◽  
Saqib Salahuddin ◽  
Hossein Javidnia
Keyword(s):  
Deep Learning ◽  
State Of The Art ◽  
Research Work ◽  
Depth Estimation ◽  
Autonomous Driving ◽  
Estimation Methods ◽  
Future Research ◽  
Comprehensive Overview ◽  
Ill Posed ◽  
Monocular Depth

Monocular depth estimation from Red-Green-Blue (RGB) images is a well-studied ill-posed problem in computer vision which has been investigated intensively over the past decade using Deep Learning (DL) approaches. The recent approaches for monocular depth estimation mostly rely on Convolutional Neural Networks (CNN). Estimating depth from two-dimensional images plays an important role in various applications including scene reconstruction, 3D object-detection, robotics and autonomous driving. This survey provides a comprehensive overview of this research topic including the problem representation and a short description of traditional methods for depth estimation. Relevant datasets and 13 state-of-the-art deep learning-based approaches for monocular depth estimation are reviewed, evaluated and discussed. We conclude this paper with a perspective towards future research work requiring further investigation in monocular depth estimation challenges.

scholarly journals Multistructure-Based Collaborative Online Distillation

Entropy ◽  
2019 ◽  
Vol 21 (4) ◽  
pp. 357
Author(s):  
Liang Gao ◽  
Xu Lan ◽  
Haibo Mi ◽  
Dawei Feng ◽  
Kele Xu ◽  
...  
Keyword(s):  
Performance Improvement ◽  
Network Performance ◽  
State Of The Art ◽  
Autonomous Driving ◽  
Supplementary Information ◽  
Training Method ◽  
Machine Learning Methods ◽  
Information Diversity ◽  
Knowledge Distillation ◽  
Memory Resources

Recently, deep learning has achieved state-of-the-art performance in more aspects than traditional shallow architecture-based machine-learning methods. However, in order to achieve higher accuracy, it is usually necessary to extend the network depth or ensemble the results of different neural networks. Increasing network depth or ensembling different networks increases the demand for memory resources and computing resources. This leads to difficulties in deploying depth-learning models in resource-constrained scenarios such as drones, mobile phones, and autonomous driving. Improving network performance without expanding the network scale has become a hot topic for research. In this paper, we propose a cross-architecture online-distillation approach to solve this problem by transmitting supplementary information on different networks. We use the ensemble method to aggregate networks of different structures, thus forming better teachers than traditional distillation methods. In addition, discontinuous distillation with progressively enhanced constraints is used to replace fixed distillation in order to reduce loss of information diversity in the distillation process. Our training method improves the distillation effect and achieves strong network-performance improvement. We used some popular models to validate the results. On the CIFAR100 dataset, AlexNet’s accuracy was improved by 5.94%, VGG by 2.88%, ResNet by 5.07%, and DenseNet by 1.28%. Extensive experiments were conducted to demonstrate the effectiveness of the proposed method. On the CIFAR10, CIFAR100, and ImageNet datasets, we observed significant improvements over traditional knowledge distillation.

scholarly journals Developing a Simulation Model for Autonomous Driving Education in the Robobo SmartCity Framework

2021 ◽  
Vol 7 (1) ◽  
pp. 49
Author(s):  
Daniel Juanatey ◽  
Martin Naya ◽  
Tamara Baamonde ◽  
Francisco Bellas
Keyword(s):  
Artificial Intelligence ◽  
Simulation Model ◽  
Smart City ◽  
State Of The Art ◽  
Autonomous Driving ◽  
The Real ◽  
Educational Framework ◽  
Real Model ◽  
Teachers And Students

This paper focuses on long-term education in Artificial Intelligence (AI) applied to robotics. Specifically, it presents the Robobo SmartCity educational framework. It is based on two main elements: the smartphone-based robot Robobo and a real model of a smart city. We describe the development of a simulation model of Robobo SmartCity in the CoppeliaSim 3D simulator, implementing both the real mock-up and the model of Robobo. In addition, a set of Python libraries that allow teachers and students to use state-of-the-art algorithms in their education projects is described too.

scholarly journals Materials that make robots smart

2019 ◽  
Vol 38 (12-13) ◽  
pp. 1338-1351 ◽  
Author(s):  
Dana Hughes ◽  
Christoffer Heckman ◽  
Nikolaus Correll
Keyword(s):  
State Of The Art ◽  
Active Role ◽  
Autonomous Driving ◽  
Community Needs ◽  
Intelligent Robots ◽  
Level Information ◽  
Brain Material ◽  
High Level ◽  
Tactile Sensations ◽  
Smart Skin

We posit that embodied artificial intelligence is not only a computational, but also a materials problem. While the importance of material and structural properties in the control loop are well understood, materials can take an active role during control by tight integration of sensors, actuators, computation, and communication. We envision such materials to abstract functionality, therefore making the construction of intelligent robots more straightforward and robust. For example, robots could be made of bones that measure load, muscles that move, skin that provides the robot with information about the kind and location of tactile sensations ranging from pressure to texture and damage, eyes that extract high-level information, and brain material that provides computation in a scalable manner. Such materials will not resemble any existing engineered materials, but rather the heterogeneous components out of which their natural counterparts are made. We describe the state-of-the-art in so-called “robotic materials,” their opportunities for revolutionizing applications ranging from manipulation to autonomous driving by describing two recent robotic materials, a smart skin and a smart tire in more depth, and conclude with open challenges that the robotics community needs to address in collaboration with allies, such as wireless sensor network researchers and polymer scientists.

Autonomous Driving Security: State of the Art and Challenges

2021 ◽  
pp. 1-1
Author(s):  
Cong Gao ◽  
Geng Wang ◽  
Weisong Shi ◽  
Zhongmin Wang ◽  
Yanping Chen
Keyword(s):  
State Of The Art ◽  
Autonomous Driving

Autonomous Vehicle Application for Improving Traffic Sign Learning near Ramps

2020 ◽  
Vol 2674 (10) ◽  
pp. 158-166
Author(s):  
Zhenhua Zhang ◽  
Leon Stenneth ◽  
Xiyuan Liu
Keyword(s):  
State Of The Art ◽  
Autonomous Vehicle ◽  
Autonomous Driving ◽  
Traffic Sign Recognition ◽  
Traffic Sign ◽  
Speed Reduction ◽  
Sign Recognition ◽  
Path Information ◽  
Real World Applications ◽  
Location Adjustment

The state-of-the-art traffic sign recognition (TSR) algorithms are designed to recognize the textual information of a traffic sign at over 95% accuracy. Even though, they are still not ready for complex roadworks near ramps. In real-world applications, when the vehicles are running on the freeway, they may misdetect the traffic signs for the ramp, which will become inaccurate feedback to the autonomous driving applications and result in unexpected speed reduction. The misdetection problems have drawn minimal attention in recent TSR studies. In this paper, it is proposed that the existing TSR studies should transform from the point-based sign recognition to path-based sign learning. In the proposed pipeline, the confidence of the TSR observations from normal vehicles can be increased by clustering and location adjustment. A supervised learning model is employed to classify the clustered learned signs and complement their path information. Test drives are conducted in 12 European countries to calibrate the models and validate the path information of the learned sign. After model implementation, the path accuracy over 1,000 learned signs can be increased from 75.04% to 89.80%. This study proves the necessity of the path-based TSR studies near freeway ramps and the proposed pipeline demonstrates a good utility and broad applicability for sensor-based autonomous vehicle applications.

scholarly journals Deep Deterministic Policy Gradient Algorithm Based on Convolutional Block Attention for Autonomous Driving

Symmetry ◽  
2021 ◽  
Vol 13 (6) ◽  
pp. 1061
Author(s):  
Yanliang Jin ◽  
Qianhong Liu ◽  
Liquan Shen ◽  
Leiji Zhu
Keyword(s):  
Reinforcement Learning ◽  
Supervised Learning ◽  
State Of The Art ◽  
Autonomous Driving ◽  
Attention Mechanism ◽  
Gradient Algorithm ◽  
Excellent Performance ◽  
Average Speed ◽  
The Road ◽  
Policy Gradient

The research on autonomous driving based on deep reinforcement learning algorithms is a research hotspot. Traditional autonomous driving requires human involvement, and the autonomous driving algorithms based on supervised learning must be trained in advance using human experience. To deal with autonomous driving problems, this paper proposes an improved end-to-end deep deterministic policy gradient (DDPG) algorithm based on the convolutional block attention mechanism, and it is called multi-input attention prioritized deep deterministic policy gradient algorithm (MAPDDPG). Both the actor network and the critic network of the model have the same structure with symmetry. Meanwhile, the attention mechanism is introduced to help the vehicles focus on useful environmental information. The experiments are conducted in the open racing car simulator (TORCS)and the results of five experiment runs on the test tracks are averaged to obtain the final result. Compared with the state-of-the-art algorithm, the maximum reward increases from 62,207 to 116,347, and the average speed increases from 135 km/h to 193 km/h, while the number of success episodes to complete a circle increases from 96 to 147. Also, the variance of the distance from the vehicle to the center of the road is compared, and the result indicates that the variance of the DDPG is 0.6 m while that of the MAPDDPG is only 0.2 m. The above results indicate that the proposed MAPDDPG achieves excellent performance.

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