3,801 research outputs found
Combining Planning and Deep Reinforcement Learning in Tactical Decision Making for Autonomous Driving
Tactical decision making for autonomous driving is challenging due to the
diversity of environments, the uncertainty in the sensor information, and the
complex interaction with other road users. This paper introduces a general
framework for tactical decision making, which combines the concepts of planning
and learning, in the form of Monte Carlo tree search and deep reinforcement
learning. The method is based on the AlphaGo Zero algorithm, which is extended
to a domain with a continuous state space where self-play cannot be used. The
framework is applied to two different highway driving cases in a simulated
environment and it is shown to perform better than a commonly used baseline
method. The strength of combining planning and learning is also illustrated by
a comparison to using the Monte Carlo tree search or the neural network policy
separately
Bicycle traffic and its interaction with motorized traffic in an agent-based transport simulation framework
Cycling as an inexpensive, healthy, and efficient mode of transport for everyday traveling is becoming increasingly popular. While many cities are promoting cycling, it is rarely included in transport models and systematic policy evaluation procedures. The purpose of this study is to extend the agent-based transport simulation framework MATSim to be able to model bicycle traffic more realistically. The network generation procedure is enriched to include attributes that are relevant for cyclists (e.g. road surfaces, slopes). Travel speed computations, plan scoring, and routing are enhanced to take into account these infrastructure attributes. The scoring, i.e. the evaluation of simulated daily travel plans, is furthermore enhanced to account for traffic events that emerge in the simulation (e.g. passings by cars), which have an additional impact on cyclists’ decisions. Inspired by an evolutionary computing perspective, a randomizing router was implemented to enable cyclists to find realistic routes. It is discussed in detail why this approach is both feasible in practical terms and also conceptually consistent with MATSim’s co-evolutionary simulation approach. It is shown that meaningful simulation results are obtained for an illustrative scenario, which indicates that the developed methods will make real-world scenarios more realistic in terms of the representation of bicycle traffic. Based on the exclusive reliance on open data, the approach is spatially transferable
Recommended from our members
A Survey on Cooperative Longitudinal Motion Control of Multiple Connected and Automated Vehicles
Learning-based social coordination to improve safety and robustness of cooperative autonomous vehicles in mixed traffic
It is expected that autonomous vehicles(AVs) and heterogeneous human-driven
vehicles(HVs) will coexist on the same road. The safety and reliability of AVs
will depend on their social awareness and their ability to engage in complex
social interactions in a socially accepted manner. However, AVs are still
inefficient in terms of cooperating with HVs and struggle to understand and
adapt to human behavior, which is particularly challenging in mixed autonomy.
In a road shared by AVs and HVs, the social preferences or individual traits of
HVs are unknown to the AVs and different from AVs, which are expected to follow
a policy, HVs are particularly difficult to forecast since they do not
necessarily follow a stationary policy. To address these challenges, we frame
the mixed-autonomy problem as a multi-agent reinforcement learning (MARL)
problem and propose an approach that allows AVs to learn the decision-making of
HVs implicitly from experience, account for all vehicles' interests, and safely
adapt to other traffic situations. In contrast with existing works, we quantify
AVs' social preferences and propose a distributed reward structure that
introduces altruism into their decision-making process, allowing the altruistic
AVs to learn to establish coalitions and influence the behavior of HVs.Comment: arXiv admin note: substantial text overlap with arXiv:2202.0088
CBPRS: A City Based Parking and Routing System
Navigational systems assist drivers in finding a route between two locations that is time optimal in theory but seldom in practice due to delaying circumstances the system is unaware of, such as traffic jams. Upon arrival at the destination the service of the system ends and the driver is forced to locate a parking place without further assistance. We propose a City Based Parking Routing System (CBPRS) that monitors and reserves parking places for CBPRS participants within a city. The CBPRS guides vehicles using an ant based distributed hierarchical routing algorithm to their reserved parking place. Through means of experiments in a simulation environment we found that reductions of travel times for participants were significant in comparison to a situation where vehicles relied on static routing information generated by the well known Dijkstra’s algorithm. Furthermore, we found that the CBPRS was able to increase city wide traffic flows and decrease the number and duration of traffic jams throughout the city once the number of participants increased.information systems;computer simulation;dynamic routing
Automated Speed and Lane Change Decision Making using Deep Reinforcement Learning
This paper introduces a method, based on deep reinforcement learning, for
automatically generating a general purpose decision making function. A Deep
Q-Network agent was trained in a simulated environment to handle speed and lane
change decisions for a truck-trailer combination. In a highway driving case, it
is shown that the method produced an agent that matched or surpassed the
performance of a commonly used reference model. To demonstrate the generality
of the method, the exact same algorithm was also tested by training it for an
overtaking case on a road with oncoming traffic. Furthermore, a novel way of
applying a convolutional neural network to high level input that represents
interchangeable objects is also introduced
- …