3 research outputs found
Efficient Uncertainty-aware Decision-making for Automated Driving Using Guided Branching
Decision-making in dense traffic scenarios is challenging for automated
vehicles (AVs) due to potentially stochastic behaviors of other traffic
participants and perception uncertainties (e.g., tracking noise and prediction
errors, etc.). Although the partially observable Markov decision process
(POMDP) provides a systematic way to incorporate these uncertainties, it
quickly becomes computationally intractable when scaled to the real-world
large-size problem. In this paper, we present an efficient uncertainty-aware
decision-making (EUDM) framework, which generates long-term lateral and
longitudinal behaviors in complex driving environments in real-time. The
computation complexity is controlled to an appropriate level by two novel
techniques, namely, the domain-specific closed-loop policy tree (DCP-Tree)
structure and conditional focused branching (CFB) mechanism. The key idea is
utilizing domain-specific expert knowledge to guide the branching in both
action and intention space. The proposed framework is validated using both
onboard sensing data captured by a real vehicle and an interactive multi-agent
simulation platform. We also release the code of our framework to accommodate
benchmarking.Comment: Accepted by IEEE International Conference on Robotics and Automation
(ICRA) 202
FP-Stereo: Hardware-Efficient Stereo Vision for Embedded Applications
Fast and accurate depth estimation, or stereo matching, is essential in
embedded stereo vision systems, requiring substantial design effort to achieve
an appropriate balance among accuracy, speed and hardware cost. To reduce the
design effort and achieve the right balance, we propose FP-Stereo for building
high-performance stereo matching pipelines on FPGAs automatically. FP-Stereo
consists of an open-source hardware-efficient library, allowing designers to
obtain the desired implementation instantly. Diverse methods are supported in
our library for each stage of the stereo matching pipeline and a series of
techniques are developed to exploit the parallelism and reduce the resource
overhead. To improve the usability, FP-Stereo can generate synthesizable C code
of the FPGA accelerator with our optimized HLS templates automatically. To
guide users for the right design choice meeting specific application
requirements, detailed comparisons are performed on various configurations of
our library to investigate the accuracy/speed/cost trade-off. Experimental
results also show that FP-Stereo outperforms the state-of-the-art FPGA design
from all aspects, including 6.08% lower error, 2x faster speed, 30% less
resource usage and 40% less energy consumption. Compared to GPU designs,
FP-Stereo achieves the same accuracy at a competitive speed while consuming
much less energy.Comment: IEEE International Conference on Field Programmable Logic and
Applications (FPL), 202
Decision-making at Unsignalized Intersection for Autonomous Vehicles: Left-turn Maneuver with Deep Reinforcement Learning
Decision-making module enables autonomous vehicles to reach appropriate
maneuvers in the complex urban environments, especially the intersection
situations. This work proposes a deep reinforcement learning (DRL) based
left-turn decision-making framework at unsignalized intersection for autonomous
vehicles. The objective of the studied automated vehicle is to make an
efficient and safe left-turn maneuver at a four-way unsignalized intersection.
The exploited DRL methods include deep Q-learning (DQL) and double DQL.
Simulation results indicate that the presented decision-making strategy could
efficaciously reduce the collision rate and improve transport efficiency. This
work also reveals that the constructed left-turn control structure has a great
potential to be applied in real-time.Comment: Some simulation results should be improved