Investigating Value of Curriculum Reinforcement Learning in Autonomous Driving Under Diverse Road and Weather Conditions
Istanbul Technical University（伊斯坦布尔理工大学）；ITU Artificial Intelligence and Data Science Research Center（伊斯坦布尔理工大学人工智能与数据科学研究中心）；Bogazici University（博阿齐奇大学）
Anil Ozturk, Mustafa Burak Gunel, Resul Dagdanov, Mirac Ekim Vural, Ferhat Yurdakul, Melih Dal, Nazim Kemal Ure
Applications of reinforcement learning (RL) are popular in autonomous driving tasks. That being said, tuning the performance of an RL agent and guaranteeing the generalization performance across variety of different driving scenarios is still largely an open problem. In particular, getting good performance on complex road and weather conditions require exhaustive tuning and computation time. Curriculum RL, which focuses on solving simpler automation tasks in order to transfer knowledge to complex tasks, is attracting attention in RL community. The main contribution of this paper is a systematic study for investigating the value of curriculum reinforcement learning in autonomous driving applications. For this purpose, we setup several different driving scenarios in a realistic driving simulator, with varying road complexity and weather conditions. Next, we train and evaluate performance of RL agents on different sequences of task combinations and curricula. Results show that curriculum RL can yield significant gains in complex driving tasks, both in terms of driving performance and sample complexity. Results also demonstrate that different curricula might enable different benefits, which hints future research directions for automated curriculum training.
Road maintenance during the Winter season is a safety critical and resource demanding operation. One of its key activities is determining road surface condition (RSC) in order to prioritize roads and allocate cleaning efforts such as plowing or salting. Two conventional approaches for determining RSC are: visual examination of roadside camera images by trained personnel and patrolling the roads to perform on-site inspections. However, with more than 500 cameras collecting images across Ontario, visual examination becomes a resource-intensive activity, difficult to scale especially during perio…
A Bi-level Framework for Traffic Accident Duration Prediction: Leveraging Weather and Road Condition Data within a Practical Optimum Pipeline
Ahsanullah University of Science and Technology（阿萨努拉科技大学）；Bangladesh University of Engineering and Technology（孟加拉工程技术大学）；Advanced Chemical Industries Limited（先进化学工业有限公司）
Deep reinforcement Learning for end-to-end driving is limited by the need of complex reward engineering. Sparse rewards can circumvent this challenge but suffers from long training time and leads to sub-optimal policy. In this work, we explore full-control driving with only goal-constrained sparse reward and propose a curriculum learning approach for end-to-end driving using only navigation view maps that benefit from small virtual-to-real domain gap. To address the complexity of multiple driving policies, we learn concurrent individual policies selected at inference by a navigation system. We…
DIDLM: A SLAM Dataset for Difficult Scenarios Featuring Infrared, Depth Cameras, LIDAR, 4D Radar, and Others under Adverse Weather, Low Light Conditions, and Rough Roads
School of Information Science and Technology, Northwest University（信息科学与技术学院，西北大学）；Department of Electrical and Computer Engineering, The University of British Columbia（电气与计算机工程系，不列颠哥伦比亚大学）
Clear Roads, Clear Vision: Advancements in Multi-Weather Restoration for Smart Transportation
Finolex Academy of Management and Technology（Finolex管理与技术学院）；Drone Lab, Centre for Artificial Intelligence and Robotics, IIT Mandi（印度理工学院曼迪分校人工智能与机器人中心无人机实验室）；Mehta Family School of Data Science and Artificial Intelligence, IIT Guwahati（印度理工学院古瓦哈提分校梅塔家族数据科学与人工智能学院）；SPACE42；Centre of Excellence: Artificial Intelligence, School of Computer Science, UPES Dehradun（UPES德拉敦分校计算机科学学院人工智能卓越中心）
The American University in Cairo（开罗美国大学）；Valeo Schalter und Sensoren GmbH（法雷奥开关与传感器有限公司）；Valeo Egypt（法雷奥埃及公司）
Conditional imitation learning (CIL) trains deep neural networks, in an end-to-end manner, to mimic human driving. This approach has demonstrated suitable vehicle control when following roads, avoiding obstacles, or taking specific turns at intersections to reach a destination. Unfortunately, performance dramatically decreases when deployed to unseen environments and is inconsistent against varying weather conditions. Most importantly, the current CIL fails to avoid static road blockages. In this work, we propose a solution to those deficiencies. First, we fuse the laser scanner with the regul…
This paper presents a novel data-driven approach to vehicle motion planning and control in off-road driving scenarios. For autonomous off-road driving, environmental conditions impact terrain traversability as a function of weather, surface composition, and slope. Geographical information system (GIS) and National Centers for Environmental Information datasets are processed to provide this information for interactive planning and control system elements. A top-level global route planner (GRP) defines optimal waypoints using dynamic programming (DP). A local path planner (LPP) computes a desire…
WEDGE: A multi-weather autonomous driving dataset built from generative vision-language models
Carnegie Mellon University（卡内基梅隆大学）；Symbiosis International University（共生国际大学）；Symbiosis Centre for Applied AI（共生应用人工智能中心）；Symbiosis Institute of Technology（共生技术学院）
