A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Deep Learning for LiDAR-Based Autonomous Vehicles in Smart Cities
https://orcid.org/https://orcid.org/0000-0003-2574-3807
https://orcid.org/https://orcid.org/0000-0002-8313-6645
https://orcid.org/https://orcid.org/0000-0002-2726-102X
Autonomous vehicles and deep learning are an integral part of smart cities. They interact and communicate with their surroundings, requiring high computer vision accuracy to maintain driver and pedestrian safety. Many autonomous vehicles leverage deep learning for detection and utilize a suite of sensors that are specific to their environment or use case. In such deep learning environment, sensor data is used as input to neural networks that make decisions regarding the vehicle’s response or reaction to its environment. These sensors in autonomous vehicles provide details regarding the vehicle’s surroundings and potential obstacles. Many sensor suites are starting to contain light detection and ranging (LiDAR) sensors, as the cost of the technology decreases and becomes more widely available. LiDAR technology uses focused light to detect distance, providing an accurate description of the sensor’s surroundings, such precise account is crucial for autonomous driving in ever-changing smart city environments. This chapter covers different applications of LiDAR technology and the use of the sensor data in deep learning applications for smart cities. A case study is also featured to illustrate a potential implementation, which is followed by discussion of future research directions.
Deep Learning for LiDAR-Based Autonomous Vehicles in Smart Cities
https://orcid.org/https://orcid.org/0000-0003-2574-3807
https://orcid.org/https://orcid.org/0000-0002-8313-6645
https://orcid.org/https://orcid.org/0000-0002-2726-102X
Autonomous vehicles and deep learning are an integral part of smart cities. They interact and communicate with their surroundings, requiring high computer vision accuracy to maintain driver and pedestrian safety. Many autonomous vehicles leverage deep learning for detection and utilize a suite of sensors that are specific to their environment or use case. In such deep learning environment, sensor data is used as input to neural networks that make decisions regarding the vehicle’s response or reaction to its environment. These sensors in autonomous vehicles provide details regarding the vehicle’s surroundings and potential obstacles. Many sensor suites are starting to contain light detection and ranging (LiDAR) sensors, as the cost of the technology decreases and becomes more widely available. LiDAR technology uses focused light to detect distance, providing an accurate description of the sensor’s surroundings, such precise account is crucial for autonomous driving in ever-changing smart city environments. This chapter covers different applications of LiDAR technology and the use of the sensor data in deep learning applications for smart cities. A case study is also featured to illustrate a potential implementation, which is followed by discussion of future research directions.
Deep Learning for LiDAR-Based Autonomous Vehicles in Smart Cities
https://orcid.org/https://orcid.org/0000-0003-2574-3807
https://orcid.org/https://orcid.org/0000-0002-8313-6645
https://orcid.org/https://orcid.org/0000-0002-2726-102X
Autonomous vehicles and deep learning are an integral part of smart cities. They interact and communicate with their surroundings, requiring high computer vision accuracy to maintain driver and pedestrian safety. Many autonomous vehicles leverage deep learning for detection and utilize a suite of sensors that are specific to their environment or use case. In such deep learning environment, sensor data is used as input to neural networks that make decisions regarding the vehicle’s response or reaction to its environment. These sensors in autonomous vehicles provide details regarding the vehicle’s surroundings and potential obstacles. Many sensor suites are starting to contain light detection and ranging (LiDAR) sensors, as the cost of the technology decreases and becomes more widely available. LiDAR technology uses focused light to detect distance, providing an accurate description of the sensor’s surroundings, such precise account is crucial for autonomous driving in ever-changing smart city environments. This chapter covers different applications of LiDAR technology and the use of the sensor data in deep learning applications for smart cities. A case study is also featured to illustrate a potential implementation, which is followed by discussion of future research directions.
Deep Learning for LiDAR-Based Autonomous Vehicles in Smart Cities
Augusto, Juan Carlos (editor) / Ponnaganti, Vinay (author) / Moh, Melody (author) / Moh, Teng-Sheng (author)
Handbook of Smart Cities ; Chapter: 65 ; 957-980
2021-07-10
24 pages
Article/Chapter (Book)
Electronic Resource
English
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Online Contents | 2011
The Future of Mobility with Connected and Autonomous Vehicles in Smart Cities
| Springer Verlag | 2019