Hybrid verification technique for decision-making of self-driving vehicles
The evolution of driving technology has recently progressed from active safety features and
ADAS systems to fully sensor-guided autonomous driving. Bringing such a vehicle to market …
ADAS systems to fully sensor-guided autonomous driving. Bringing such a vehicle to market …
Integrated decision making and planning framework for autonomous vehicle considering uncertain prediction of surrounding vehicles
C Tang, Y Liu, H Xiao, L Xiong - 2022 IEEE 25th International …, 2022 - ieeexplore.ieee.org
Uncertainties in dynamical driving environments are crucial to the decision making and
trajectory planning modules for autonomous vehicles. Without proper handling of such …
trajectory planning modules for autonomous vehicles. Without proper handling of such …
Vehicle turning behavior modeling at conflicting areas of mixed-flow intersections based on deep learning
Performing a left turn in a non-protected phase at mixed-flow intersections is one of the most
challenging driving maneuvers. In general, there are three typical behavioral features during …
challenging driving maneuvers. In general, there are three typical behavioral features during …
Human-like maneuver decision using LSTM-CRF model for on-road self-driving
In the near future, self-driving vehicles will be frequently tested in urban traffic, and will
definitely coexist with human-driving vehicles. To harmoniously share traffic resources, self …
definitely coexist with human-driving vehicles. To harmoniously share traffic resources, self …
Multi-agent interactions modeling with correlated policies
In multi-agent systems, complex interacting behaviors arise due to the high correlations
among agents. However, previous work on modeling multi-agent interactions from …
among agents. However, previous work on modeling multi-agent interactions from …
A novel network architecture of decision-making for self-driving vehicles based on long short-term memory and grasshopper optimization algorithm
Y Shi, Y Li, J Fan, T Wang, T Yin - IEEE Access, 2020 - ieeexplore.ieee.org
Long short-term memory network is one of the most important network architectures of
decision-making for self-driving vehicles. Nevertheless, the decision-making accuracy of …
decision-making for self-driving vehicles. Nevertheless, the decision-making accuracy of …
A driving behavior awareness model based on a dynamic Bayesian network and distributed genetic algorithm
It is necessary for automated vehicles (AVs) and advanced driver assistance systems
(ADASs) to have a better understanding of the traffic environment including driving …
(ADASs) to have a better understanding of the traffic environment including driving …
Anytime safety verification of autonomous vehicles
F Gruber, M Althoff - 2018 21st International Conference on …, 2018 - ieeexplore.ieee.org
We propose a procedure to formally verify the safety of autonomous vehicles online, ie,
during operation, that considers the uniqueness of each traffic situation. A challenging …
during operation, that considers the uniqueness of each traffic situation. A challenging …
High-speed highway scene prediction based on driver models learned from demonstrations
DS González, JS Dibangoye… - 2016 IEEE 19th …, 2016 - ieeexplore.ieee.org
One of the key factors to ensure the safe operation of autonomous and semi-autonomous
vehicles in dynamic environments is the ability to accurately predict the motion of the …
vehicles in dynamic environments is the ability to accurately predict the motion of the …
Generic vehicle tracking framework capable of handling occlusions based on modified mixture particle filter
Accurate and robust tracking of surrounding road participants plays an important role in
autonomous driving. However, there is usually no prior knowledge of the number of tracking …
autonomous driving. However, there is usually no prior knowledge of the number of tracking …