3,329 research outputs found
Driving with Style: Inverse Reinforcement Learning in General-Purpose Planning for Automated Driving
Behavior and motion planning play an important role in automated driving.
Traditionally, behavior planners instruct local motion planners with predefined
behaviors. Due to the high scene complexity in urban environments,
unpredictable situations may occur in which behavior planners fail to match
predefined behavior templates. Recently, general-purpose planners have been
introduced, combining behavior and local motion planning. These general-purpose
planners allow behavior-aware motion planning given a single reward function.
However, two challenges arise: First, this function has to map a complex
feature space into rewards. Second, the reward function has to be manually
tuned by an expert. Manually tuning this reward function becomes a tedious
task. In this paper, we propose an approach that relies on human driving
demonstrations to automatically tune reward functions. This study offers
important insights into the driving style optimization of general-purpose
planners with maximum entropy inverse reinforcement learning. We evaluate our
approach based on the expected value difference between learned and
demonstrated policies. Furthermore, we compare the similarity of human driven
trajectories with optimal policies of our planner under learned and
expert-tuned reward functions. Our experiments show that we are able to learn
reward functions exceeding the level of manual expert tuning without prior
domain knowledge.Comment: Appeared at IROS 2019. Accepted version. Added/updated footnote,
minor correction in preliminarie
Navigating Occluded Intersections with Autonomous Vehicles using Deep Reinforcement Learning
Providing an efficient strategy to navigate safely through unsignaled
intersections is a difficult task that requires determining the intent of other
drivers. We explore the effectiveness of Deep Reinforcement Learning to handle
intersection problems. Using recent advances in Deep RL, we are able to learn
policies that surpass the performance of a commonly-used heuristic approach in
several metrics including task completion time and goal success rate and have
limited ability to generalize. We then explore a system's ability to learn
active sensing behaviors to enable navigating safely in the case of occlusions.
Our analysis, provides insight into the intersection handling problem, the
solutions learned by the network point out several shortcomings of current
rule-based methods, and the failures of our current deep reinforcement learning
system point to future research directions.Comment: IEEE International Conference on Robotics and Automation (ICRA 2018
End-to-End Learning of Driving Models with Surround-View Cameras and Route Planners
For human drivers, having rear and side-view mirrors is vital for safe
driving. They deliver a more complete view of what is happening around the car.
Human drivers also heavily exploit their mental map for navigation.
Nonetheless, several methods have been published that learn driving models with
only a front-facing camera and without a route planner. This lack of
information renders the self-driving task quite intractable. We investigate the
problem in a more realistic setting, which consists of a surround-view camera
system with eight cameras, a route planner, and a CAN bus reader. In
particular, we develop a sensor setup that provides data for a 360-degree view
of the area surrounding the vehicle, the driving route to the destination, and
low-level driving maneuvers (e.g. steering angle and speed) by human drivers.
With such a sensor setup we collect a new driving dataset, covering diverse
driving scenarios and varying weather/illumination conditions. Finally, we
learn a novel driving model by integrating information from the surround-view
cameras and the route planner. Two route planners are exploited: 1) by
representing the planned routes on OpenStreetMap as a stack of GPS coordinates,
and 2) by rendering the planned routes on TomTom Go Mobile and recording the
progression into a video. Our experiments show that: 1) 360-degree
surround-view cameras help avoid failures made with a single front-view camera,
in particular for city driving and intersection scenarios; and 2) route
planners help the driving task significantly, especially for steering angle
prediction.Comment: to be published at ECCV 201
Motion Planning for Autonomous Driving: The State of the Art and Future Perspectives
Thanks to the augmented convenience, safety advantages, and potential
commercial value, Intelligent vehicles (IVs) have attracted wide attention
throughout the world. Although a few autonomous driving unicorns assert that
IVs will be commercially deployable by 2025, their implementation is still
restricted to small-scale validation due to various issues, among which precise
computation of control commands or trajectories by planning methods remains a
prerequisite for IVs. This paper aims to review state-of-the-art planning
methods, including pipeline planning and end-to-end planning methods. In terms
of pipeline methods, a survey of selecting algorithms is provided along with a
discussion of the expansion and optimization mechanisms, whereas in end-to-end
methods, the training approaches and verification scenarios of driving tasks
are points of concern. Experimental platforms are reviewed to facilitate
readers in selecting suitable training and validation methods. Finally, the
current challenges and future directions are discussed. The side-by-side
comparison presented in this survey not only helps to gain insights into the
strengths and limitations of the reviewed methods but also assists with
system-level design choices.Comment: 20 pages, 14 figures and 5 table
Weakly Supervised Reinforcement Learning for Autonomous Highway Driving via Virtual Safety Cages
The use of neural networks and reinforcement learning has become increasingly
popular in autonomous vehicle control. However, the opaqueness of the resulting
control policies presents a significant barrier to deploying neural
network-based control in autonomous vehicles. In this paper, we present a
reinforcement learning based approach to autonomous vehicle longitudinal
control, where the rule-based safety cages provide enhanced safety for the
vehicle as well as weak supervision to the reinforcement learning agent. By
guiding the agent to meaningful states and actions, this weak supervision
improves the convergence during training and enhances the safety of the final
trained policy. This rule-based supervisory controller has the further
advantage of being fully interpretable, thereby enabling traditional validation
and verification approaches to ensure the safety of the vehicle. We compare
models with and without safety cages, as well as models with optimal and
constrained model parameters, and show that the weak supervision consistently
improves the safety of exploration, speed of convergence, and model
performance. Additionally, we show that when the model parameters are
constrained or sub-optimal, the safety cages can enable a model to learn a safe
driving policy even when the model could not be trained to drive through
reinforcement learning alone.Comment: Published in Sensor
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