A Survey of Autonomous Driving: Common Practices and Emerging Technologies
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Summary
The technical aspect of automated driving is surveyed, with an overview of available datasets and tools for ADS development and many state-of-the-art algorithms implemented and compared on their own platform in a real-world driving setting.
- Type
- article
- Published
- 2019-06-12
- Cited by
- 1,904
- References
- 312
- Access
- Open access
- OpenAlex
- https://openalex.org/W2953303875
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:186206717
Keywords
Robustness (evolution), Computer science, Perception, Emerging technologies, Data science
References
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- Auditory interfaces in automated driving: an international survey
- Driving Signature Extraction
- Fast LIDAR localization using multiresolution Gaussian mixture maps
- Sampling-based algorithms for optimal motion planning
- You Only Look Once: Unified, Real-Time Object Detection
- The DARPA Urban Challenge: Autonomous Vehicles in City Traffic, George Air Force Base, Victorville, California, USA
- An investigation on the effect of driver style and driving events on energy demand of a PHEV
- Vehicle dynamics and control
- Robot Motion Planning and Control
- Vehicle localization using mono-camera and geo-referenced traffic signs
- Integration of GPS and dead-reckoning navigation systems
- Traffic trajectory history and drive path generation using GPS data cloud
- It's not easy seeing green: Lighting-resistant stereo Visual Teach & Repeat using color-constant images
- Hybrid GPS-GSM Localization of Automobile Tracking System
- Autonomous convoy driving by night: The vehicle tracking system
Cited by
- Effects of Image Quality on the Accuracy Human Pose Estimation and Detection of Eye Lid Opening/Closing Using Openpose and DLib
- nuScenes: A Multimodal Dataset for Autonomous Driving
- Artificial intelligence: Implications for the future of work.
- Driving-task-related human-machine interaction in automated driving: towards a bigger picture
- Longitudinal Motion Planning for Autonomous Vehicles and Its Impact on Congestion: A Survey
- Performance Enhancement of INS/GNSS/Refreshed-SLAM Integration for Acceptable Lane-Level Navigation Accuracy
- Interpretable End-to-End Urban Autonomous Driving With Latent Deep Reinforcement Learning
- Developments in Modern GNSS and Its Impact on Autonomous Vehicle Architectures
- Integrating Deep Reinforcement Learning with Model-based Path Planners for Automated Driving
- An RLS-Based Instantaneous Velocity Estimator for Extended Radar Tracking
- Overview of Tools Supporting Planning for Automated Driving
- A Survey of End-to-End Driving: Architectures and Training Methods
- Deep Neural Network Perception Models and Robust Autonomous Driving Systems: Practical Solutions for Mitigation and Improvement
- Extracting Human-Like Driving Behaviors From Expert Driver Data Using Deep Learning
- Hindsight Reward Shaping in Deep Reinforcement Learning
- End-to-end Autonomous Driving Perception with Sequential Latent Representation Learning
- Enhance The Performance Of Navigation: A Two-Stage Machine Learning Approach
- A Universal Control System for Self-Driving Car Towards Urban Challenges
- VTGNet: A Vision-Based Trajectory Generation Network for Autonomous Vehicles in Urban Environments
- K-EmoCon, a multimodal sensor dataset for continuous emotion recognition in naturalistic conversations
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