48 research outputs found
Exploring the Limitations of Behavior Cloning for Autonomous Driving
Driving requires reacting to a wide variety of complex environment conditions
and agent behaviors. Explicitly modeling each possible scenario is unrealistic.
In contrast, imitation learning can, in theory, leverage data from large fleets
of human-driven cars. Behavior cloning in particular has been successfully used
to learn simple visuomotor policies end-to-end, but scaling to the full
spectrum of driving behaviors remains an unsolved problem. In this paper, we
propose a new benchmark to experimentally investigate the scalability and
limitations of behavior cloning. We show that behavior cloning leads to
state-of-the-art results, including in unseen environments, executing complex
lateral and longitudinal maneuvers without these reactions being explicitly
programmed. However, we confirm well-known limitations (due to dataset bias and
overfitting), new generalization issues (due to dynamic objects and the lack of
a causal model), and training instability requiring further research before
behavior cloning can graduate to real-world driving. The code of the studied
behavior cloning approaches can be found at
https://github.com/felipecode/coiltraine
Interpretable Motion Planner for Urban Driving via Hierarchical Imitation Learning
Learning-based approaches have achieved remarkable performance in the domain
of autonomous driving. Leveraging the impressive ability of neural networks and
large amounts of human driving data, complex patterns and rules of driving
behavior can be encoded as a model to benefit the autonomous driving system.
Besides, an increasing number of data-driven works have been studied in the
decision-making and motion planning module. However, the reliability and the
stability of the neural network is still full of uncertainty. In this paper, we
introduce a hierarchical planning architecture including a high-level
grid-based behavior planner and a low-level trajectory planner, which is highly
interpretable and controllable. As the high-level planner is responsible for
finding a consistent route, the low-level planner generates a feasible
trajectory. We evaluate our method both in closed-loop simulation and real
world driving, and demonstrate the neural network planner has outstanding
performance in complex urban autonomous driving scenarios.Comment: 6 pages, 8 figures, accepted by IROS202
Imitative Planning using Conditional Normalizing Flow
We explore the application of normalizing flows for improving the performance
of trajectory planning for autonomous vehicles (AVs). Normalizing flows provide
an invertible mapping from a known prior distribution to a potentially complex,
multi-modal target distribution and allow for fast sampling with exact PDF
inference. By modeling a trajectory planner's cost manifold as an energy
function we learn a scene conditioned mapping from the prior to a Boltzmann
distribution over the AV control space. This mapping allows for control samples
and their associated energy to be generated jointly and in parallel. We propose
using neural autoregressive flow (NAF) as part of an end-to-end deep learned
system that allows for utilizing sensors, map, and route information to
condition the flow mapping. Finally, we demonstrate the effectiveness of our
approach on real world datasets over IL and hand constructed trajectory
sampling techniques.Comment: Submittted to 4th Conference on Robot Learning (CoRL 2020), Cambridge
MA, US
Training Adversarial Agents to Exploit Weaknesses in Deep Control Policies
Deep learning has become an increasingly common technique for various control
problems, such as robotic arm manipulation, robot navigation, and autonomous
vehicles. However, the downside of using deep neural networks to learn control
policies is their opaque nature and the difficulties of validating their
safety. As the networks used to obtain state-of-the-art results become
increasingly deep and complex, the rules they have learned and how they operate
become more challenging to understand. This presents an issue, since in
safety-critical applications the safety of the control policy must be ensured
to a high confidence level. In this paper, we propose an automated black box
testing framework based on adversarial reinforcement learning. The technique
uses an adversarial agent, whose goal is to degrade the performance of the
target model under test. We test the approach on an autonomous vehicle problem,
by training an adversarial reinforcement learning agent, which aims to cause a
deep neural network-driven autonomous vehicle to collide. Two neural networks
trained for autonomous driving are compared, and the results from the testing
are used to compare the robustness of their learned control policies. We show
that the proposed framework is able to find weaknesses in both control policies
that were not evident during online testing and therefore, demonstrate a
significant benefit over manual testing methods.Comment: 2020 IEEE International Conference on Robotics and Automation (ICRA