Continuous improvement of self-driving cars using dynamic confidence-aware reinforcement learning
Today's self-driving vehicles have achieved impressive driving capabilities, but still suffer
from uncertain performance in long-tail cases. Training a reinforcement-learning-based self …
from uncertain performance in long-tail cases. Training a reinforcement-learning-based self …
Camo-mot: Combined appearance-motion optimization for 3d multi-object tracking with camera-lidar fusion
3D Multi-object tracking (MOT) ensures consistency during continuous dynamic detection,
conducive to subsequent motion planning and navigation tasks in autonomous driving …
conducive to subsequent motion planning and navigation tasks in autonomous driving …
Cognitive-based crack detection for road maintenance: an integrated system in cyber-physical-social systems
Effective road maintenance can not only achieve a balance between limited resources and
long-term high-efficiency performance of road but also reduce the loss of life and property …
long-term high-efficiency performance of road but also reduce the loss of life and property …
Mixed cloud control testbed: Validating vehicle-road-cloud integration via mixed digital twin
Reliable and efficient validation technologies are critical for the recent development of multi-
vehicle cooperation and vehicle-road-cloud integration. In this paper, we introduce our …
vehicle cooperation and vehicle-road-cloud integration. In this paper, we introduce our …
A multi-vehicle game-theoretic framework for decision making and planning of autonomous vehicles in mixed traffic
To improve the safety, comfort, and efficiency of the intelligent transportation system,
particularly in complex traffic environments where autonomous vehicles (AVs) and human …
particularly in complex traffic environments where autonomous vehicles (AVs) and human …
POMDP motion planning algorithm based on multi-modal driving intention
L Li, W Zhao, C Wang - IEEE Transactions on Intelligent …, 2022 - ieeexplore.ieee.org
On highways, the interaction with surrounding vehicles is very crucial for the decision-
making and planning of autonomous vehicles. However, the multi-modal driving intentions …
making and planning of autonomous vehicles. However, the multi-modal driving intentions …
Long-tail prediction uncertainty aware trajectory planning for self-driving vehicles
A typical trajectory planner of self-driving vehicles commonly relies on predicting the future
behavior of surrounding obstacles. Recently, deep learning technology has been widely …
behavior of surrounding obstacles. Recently, deep learning technology has been widely …
Real-Time Local Greedy Search for Multiaxis Globally Time-Optimal Trajectory
Time-optimal trajectory planning aims to minimize the traversal time of arbitrary geometric
paths. The demand for real-time planning widely exists in robotics, numerical control …
paths. The demand for real-time planning widely exists in robotics, numerical control …
Vehicle-in-Virtual-Environment (VVE) method for autonomous driving system development, evaluation and demonstration
The current approach to connected and autonomous driving function development and
evaluation uses model-in-the-loop simulation, hardware-in-the-loop simulation and limited …
evaluation uses model-in-the-loop simulation, hardware-in-the-loop simulation and limited …
Human-Guided Deep Reinforcement Learning for Optimal Decision Making of Autonomous Vehicles
Although deep reinforcement learning (DRL) methods are promising for making behavioral
decisions in autonomous vehicles (AVs), their low training efficiency and difficulty to adapt to …
decisions in autonomous vehicles (AVs), their low training efficiency and difficulty to adapt to …