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Automobile Driving

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Dense reinforcement learning for safety validation of autonomous vehicles.

Nature
One critical bottleneck that impedes the development and deployment of autonomous vehicles is the prohibitively high economic and time costs required to validate their safety in a naturalistic driving environment, owing to the rarity of safety-critic...

Autonomous Driving Control Based on the Technique of Semantic Segmentation.

Sensors (Basel, Switzerland)
Advanced Driver Assistance Systems (ADAS) are only applied to relatively simple scenarios, such as highways. If there is an emergency while driving, the driver should take control of the car to deal properly with the situation at any time. Obviously,...

High-performance and low-power source-gated transistors enabled by a solution-processed metal oxide homojunction.

Proceedings of the National Academy of Sciences of the United States of America
Cost-effective fabrication of mechanically flexible low-power electronics is important for emerging applications including wearable electronics, artificial intelligence, and the Internet of Things. Here, solution-processed source-gated transistors (S...

Deep Learning with Attention Mechanisms for Road Weather Detection.

Sensors (Basel, Switzerland)
There is great interest in automatically detecting road weather and understanding its impacts on the overall safety of the transport network. This can, for example, support road condition-based maintenance or even serve as detection systems that assi...

How Do Autonomous Vehicles Decide?

Sensors (Basel, Switzerland)
The advancement in sensor technologies, mobile network technologies, and artificial intelligence has pushed the boundaries of different verticals, e.g., eHealth and autonomous driving. Statistics show that more than one million people are killed in t...

Unusual Driver Behavior Detection in Videos Using Deep Learning Models.

Sensors (Basel, Switzerland)
Anomalous driving behavior detection is becoming more popular since it is vital in ensuring the safety of drivers and passengers in vehicles. Road accidents happen for various reasons, including health, mental stress, and fatigue. It is critical to m...

Multimodal Warnings Design for In-Vehicle Robots under Driving Safety Scenarios.

Sensors (Basel, Switzerland)
In case of dangerous driving, the in-vehicle robot can provide multimodal warnings to help the driver correct the wrong operation, so the impact of the warning signal itself on driving safety needs to be reduced. This study investigates the design of...

Guided Depth Completion with Instance Segmentation Fusion in Autonomous Driving Applications.

Sensors (Basel, Switzerland)
Pixel-level depth information is crucial to many applications, such as autonomous driving, robotics navigation, 3D scene reconstruction, and augmented reality. However, depth information, which is usually acquired by sensors such as LiDAR, is sparse....

Deep Deterministic Policy Gradient-Based Autonomous Driving for Mobile Robots in Sparse Reward Environments.

Sensors (Basel, Switzerland)
In this paper, we propose a deep deterministic policy gradient (DDPG)-based path-planning method for mobile robots by applying the hindsight experience replay (HER) technique to overcome the performance degradation resulting from sparse reward proble...

A Study on Object Detection Performance of YOLOv4 for Autonomous Driving of Tram.

Sensors (Basel, Switzerland)
Recently, autonomous driving technology has been in the spotlight. However, autonomous driving is still in its infancy in the railway industry. In the case of railways, there are fewer control elements than autonomous driving of cars due to the chara...