DeepEMplanner: An EM Motion Planner with Iterative Interactions
Motion planning is a computational problem that finds a sequence of valid trajectories, often
based on surrounding agents' forecasting, environmental understanding, and historical and …
based on surrounding agents' forecasting, environmental understanding, and historical and …
DeepSIL: A software-in-the-loop framework for evaluating motion planning schemes using multiple trajectory prediction networks
J Strohbeck, J Müller, A Holzbock… - 2021 IEEE/RSJ …, 2021 - ieeexplore.ieee.org
Testing and verification is still an open issue on the way to fully automated driving.
Simulations can help to reduce the required testing efforts, however, classical simulators …
Simulations can help to reduce the required testing efforts, however, classical simulators …
Learning to obey traffic rules using constrained policy optimization
When planning motions for autonomous vehicles, traffic rules must be obeyed to ensure
safety and reject liability claims. However, present solutions do not scale well with the …
safety and reject liability claims. However, present solutions do not scale well with the …
CommonRoad-RL: A configurable reinforcement learning environment for motion planning of autonomous vehicles
Reinforcement learning (RL) methods have gained popularity in the field of motion planning
for autonomous vehicles due to their success in robotics and computer games. However, no …
for autonomous vehicles due to their success in robotics and computer games. However, no …
Mapper: Multi-agent path planning with evolutionary reinforcement learning in mixed dynamic environments
Multi-agent navigation in dynamic environments is of great industrial value when deploying
a large scale fleet of robot to real-world applications. This paper proposes a decentralized …
a large scale fleet of robot to real-world applications. This paper proposes a decentralized …
Mats: An interpretable trajectory forecasting representation for planning and control
Reasoning about human motion is a core component of modern human-robot interactive
systems. In particular, one of the main uses of behavior prediction in autonomous systems is …
systems. In particular, one of the main uses of behavior prediction in autonomous systems is …
End-to-end interactive prediction and planning with optical flow distillation for autonomous driving
With the recent advancement of deep learning technology, data-driven approaches for
autonomous car prediction and planning have achieved extraordinary performance …
autonomous car prediction and planning have achieved extraordinary performance …
Motion Planner with Fixed-Horizon Constrained Reinforcement Learning for Complex Autonomous Driving Scenarios
In autonomous driving, behavioral decision-making and trajectory planning remain huge
challenges due to the large amount of uncertainty in environments and complex interaction …
challenges due to the large amount of uncertainty in environments and complex interaction …
Motionlm: Multi-agent motion forecasting as language modeling
Reliable forecasting of the future behavior of road agents is a critical component to safe
planning in autonomous vehicles. Here, we represent continuous trajectories as sequences …
planning in autonomous vehicles. Here, we represent continuous trajectories as sequences …
Safetynet: Safe planning for real-world self-driving vehicles using machine-learned policies
In this paper we present the first safe system for full control of self-driving vehicles trained
from human demonstrations and deployed in challenging, real-world, urban environments …
from human demonstrations and deployed in challenging, real-world, urban environments …