Versatile Scene-Consistent Traffic Scenario Generation as Optimization with Diffusion
Generating realistic and controllable agent behaviors in traffic simulation is crucial for the
development of autonomous vehicles. This problem is often formulated as imitation learning …
development of autonomous vehicles. This problem is often formulated as imitation learning …
LASIL: Learner-Aware Supervised Imitation Learning For Long-term Microscopic Traffic Simulation
Microscopic traffic simulation plays a crucial role in transportation engineering by providing
insights into individual vehicle behavior and overall traffic flow. However creating a realistic …
insights into individual vehicle behavior and overall traffic flow. However creating a realistic …
Behaviorally diverse traffic simulation via reinforcement learning
S Shiroshita, S Maruyama, D Nishiyama… - 2020 IEEE/RSJ …, 2020 - ieeexplore.ieee.org
Traffic simulators are important tools in autonomous driving development. While continuous
progress has been made to provide developers more options for modeling various traffic …
progress has been made to provide developers more options for modeling various traffic …
Simulating emergent properties of human driving behavior using multi-agent reward augmented imitation learning
Recent developments in multi-agent imitation learning have shown promising results for
modeling the behavior of human drivers. However, it is challenging to capture emergent …
modeling the behavior of human drivers. However, it is challenging to capture emergent …
MRIC: Model-Based Reinforcement-Imitation Learning with Mixture-of-Codebooks for Autonomous Driving Simulation
B He, Y Li - arXiv preprint arXiv:2404.18464, 2024 - arxiv.org
Accurately simulating diverse behaviors of heterogeneous agents in various scenarios is
fundamental to autonomous driving simulation. This task is challenging due to the multi …
fundamental to autonomous driving simulation. This task is challenging due to the multi …
Learning realistic traffic agents in closed-loop
Realistic traffic simulation is crucial for developing self-driving software in a safe and
scalable manner prior to real-world deployment. Typically, imitation learning (IL) is used to …
scalable manner prior to real-world deployment. Typically, imitation learning (IL) is used to …
Trafficsim: Learning to simulate realistic multi-agent behaviors
Simulation has the potential to massively scale evaluation of self-driving systems, enabling
rapid development as well as safe deployment. Bridging the gap between simulation and …
rapid development as well as safe deployment. Bridging the gap between simulation and …
CtRL-Sim: Reactive and Controllable Driving Agents with Offline Reinforcement Learning
Evaluating autonomous vehicle stacks (AVs) in simulation typically involves replaying
driving logs from real-world recorded traffic. However, agents replayed from offline data do …
driving logs from real-world recorded traffic. However, agents replayed from offline data do …
Trajgen: Generating realistic and diverse trajectories with reactive and feasible agent behaviors for autonomous driving
Q Zhang, Y Gao, Y Zhang, Y Guo… - IEEE Transactions …, 2022 - ieeexplore.ieee.org
Realistic and diverse simulation scenarios with reactive and feasible agent behaviors can
be used for validation and verification of self-driving system performance without relying on …
be used for validation and verification of self-driving system performance without relying on …
Learning Realistic and Reactive Traffic Agents
M Zhu, D Chen, X Yuan, Z Shang… - 2024 IEEE Intelligent …, 2024 - ieeexplore.ieee.org
In recent years, remarkable strides have been made in the field of autonomous driving, with
a particular focus on enhancing perception and prediction capabilities through the …
a particular focus on enhancing perception and prediction capabilities through the …