English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Decision making for autonomous driving in urban environments is challengingdue to the complexity of the road structure and the uncertainty in the behaviorof diverse road users. Traditional methods consist of manually designed rulesas the driving policy, which require expert domain knowledge, are difficult togeneralize and might give sub-optimal results as the environment gets complex.Whereas, using reinforcement learning, optimal driving policy could be learnedand improved automatically through several interactions with the environment.However, current research in the field of reinforcement learning for autonomousdriving is mainly focused on highway setup with little to no emphasis on urbanenvironments. In this work, a deep reinforcement learning based decision-makingapproach for high-level driving behavior is proposed for urban environments inthe presence of pedestrians. For this, the use of Deep Recurrent Q-Network(DRQN) is explored, a method combining state-of-the art Deep Q-Network (DQN)with a long term short term memory (LSTM) layer helping the agent gain a memoryof the environment. A 3-D state representation is designed as the inputcombined with a well defined reward function to train the agent for learning anappropriate behavior policy in a real-world like urban simulator. The proposedmethod is evaluated for dense urban scenarios and compared with a rule-basedapproach and results show that the proposed DRQN based driving behaviordecision maker outperforms the rule-based approach.

Behavioral decision-making for urban autonomous driving in the presence of pedestrians using Deep Recurrent Q-Network