Low-latency Perception in Off-Road Dynamical Low Visibility Environments
Federal University of Bahia（巴伊亚联邦大学）；Brazilian Institute of Robotics, SENAI CIMATEC（巴西机器人研究所，SENAI CIMATEC）；SENAI CIMATEC
Nelson Alves, Marco Ruiz, Marco Reis, Tiago Cajahyba, Davi Oliveira, Ana Barreto, Eduardo F. Simas Filho, Wagner L. A. de Oliveira, Leizer Schnitman, Roberto L. S. Monteiro
This work proposes a perception system for autonomous vehicles and advanced driver assistance specialized on unpaved roads and off-road environments. In this research, the authors have investigated the behavior of Deep Learning algorithms applied to semantic segmentation of off-road environments and unpaved roads under differents adverse conditions of visibility. Almost 12,000 images of different unpaved and off-road environments were collected and labeled. It was assembled an off-road proving ground exclusively for its development. The proposed dataset also contains many adverse situations such as rain, dust, and low light. To develop the system, we have used convolutional neural networks trained to segment obstacles and areas where the car can pass through. We developed a Configurable Modular Segmentation Network (CMSNet) framework to help create different architectures arrangements and test them on the proposed dataset. Besides, we also have ported some CMSNet configurations by removing and fusing many layers using TensorRT, C++, and CUDA to achieve embedded real-time inference and allow field tests. The main contributions of this work are: a new dataset for unpaved roads and off-roads environments containing many adverse conditions such as night, rain, and dust; a CMSNet framework; an investigation regarding the feasibility of applying deep learning to detect region where the vehicle can pass through when there is no clear boundary of the track; a study of how our proposed segmentation algorithms behave in different severity levels of visibility impairment; and an evaluation of field tests carried out with semantic segmentation architectures ported for real-time inference.
We present an approach towards robust lane tracking for assisted and autonomous driving, particularly under poor visibility. Autonomous detection of lane markers improves road safety, and purely visual tracking is desirable for widespread vehicle compatibility and reducing sensor intrusion, cost, and energy consumption. However, visual approaches are often ineffective because of a number of factors, including but not limited to occlusion, poor weather conditions, and paint wear-off. Our method, named SafeDrive, attempts to improve visual lane detection approaches in drastically degraded visual…
Latency-aware Road Anomaly Segmentation in Videos: A Photorealistic Dataset and New Metrics
AIR, Tsinghua University（清华大学智能产业研究院）；Beijing Institute of Technology（北京理工大学）；Chinese Academy of Science（中国科学院）；Hong Kong Polytechnic University（香港理工大学）；Mercedes-Benz Group China Ltd.（梅赛德斯-奔驰中国有限公司）
Learning to recover clear images from images having a combination of degrading factors is a challenging task. That being said, autonomous surveillance in low visibility conditions caused by high pollution/smoke, poor air quality index, low light, atmospheric scattering, and haze during a blizzard becomes even more important to prevent accidents. It is thus crucial to form a solution that can result in a high-quality image and is efficient enough to be deployed for everyday use. However, the lack of proper datasets available to tackle this task limits the performance of the previous methods pro…
Quantile-Physics Hybrid Framework for Safe-Speed Recommendation under Diverse Weather Conditions Leveraging Connected Vehicle and Road Weather Information Systems Data
Stanford University（斯坦福大学）；University of California, Santa Barbara（加州大学圣巴巴拉分校）
Traffic congestion has large economic and social costs. The introduction of autonomous vehicles can potentially reduce this congestion, both by increasing network throughput and by enabling a social planner to incentivize users of autonomous vehicles to take longer routes that can alleviate congestion on more direct roads. We formalize the effects of altruistic autonomy on roads shared between human drivers and autonomous vehicles. In this work, we develop a formal model of road congestion on shared roads based on the fundamental diagram of traffic. We consider a network of parallel roads and …
Massive amounts of satellite data have been gathered over time, holding the potential to unveil a spatiotemporal chronicle of the surface of Earth. These data allow scientists to investigate various important issues, such as land use changes, on a global scale. However, not all land-use phenomena are equally visible on satellite imagery. In particular, the creation of an inventory of the planet's road infrastructure remains a challenge, despite being crucial to analyze urbanization patterns and their impact. Towards this end, this work advances data-driven approaches for the automatic identifi…
Following detection and tracking of traffic actors, prediction of their future motion is the next critical component of a self-driving vehicle (SDV) technology, allowing the SDV to operate safely and efficiently in its environment. This is particularly important when it comes to vulnerable road users (VRUs), such as pedestrians and bicyclists. These actors need to be handled with special care due to an increased risk of injury, as well as the fact that their behavior is less predictable than that of motorized actors. To address this issue, in the current study we present a deep learning-based …
LCPC（法国中央桥梁与道路实验室）；University Paris Est（巴黎东大学）；INRETS（法国国家交通与安全研究所）；Mines ParisTech（巴黎高科矿业学院）
Visibility distance on the road pathway plays a significant role in road safety and in particular, has a clear impact on the choice of speed limits. Visibility distance is thus of importance for road engineers and authorities. While visibility distance criteria are routinely taken into account in road design, only a few systems exist for estimating it on existing road networks. Most existing systems comprise a target vehicle followed at a constant distance by an observer vehicle, which only allows to check if a given, fixed visibility distance is available. We propose two new approaches that a…
We present a machine learning approach for estimating the second derivative of a drivable surface, its roughness. Robot perception generally focuses on the first derivative, obstacle detection. However, the second derivative is also important due to its direct relation (with speed) to the shock the vehicle experiences. Knowing the second derivative allows a vehicle to slow down in advance of rough terrain. Estimating the second derivative is challenging due to uncertainty. For example, at range, laser readings may be so sparse that significant information about the surface is missing. Also, a …
We introduce a motion forecasting (behavior prediction) method that meets the latency requirements for autonomous driving in dense urban environments without sacrificing accuracy. A whole-scene sparse input representation allows StopNet to scale to predicting trajectories for hundreds of road agents with reliable latency. In addition to predicting trajectories, our scene encoder lends itself to predicting whole-scene probabilistic occupancy grids, a complementary output representation suitable for busy urban environments. Occupancy grids allow the AV to reason collectively about the behavior o…
Delay-Aware Reinforcement Learning for Highway On-Ramp Merging under Stochastic Communication Latency
Department of Computer Science, George Washington University, Washington, D.C.（计算机科学系，乔治华盛顿大学，华盛顿特区）；Connected and Automated Vehicle Program Manager, Traffic Operations Division, Virginia Department of Transportation（连接与自动化车辆计划主任，交通运营处，弗吉尼亚州交通部）；Department of Mechanical & Aerospace Engineering, George Washington University, Washington, D.C.（机械与航空航天工程系，乔治华盛顿大学，华盛顿特区）
Recently, autonomous driving development ignited competition among car makers and technical corporations. Low-level automation cars are already commercially available. But high automated vehicles where the vehicle drives by itself without human monitoring is still at infancy. Such autonomous vehicles (AVs) rely on the computing system in the car to to interpret the environment and make driving decisions. Therefore, computing system design is essential particularly in enhancing the attainment of driving safety. However, to our knowledge, no clear guideline exists so far regarding safety-aware A…
MAD: Mapping-Aware World Models for Agile Quadrotor Flight
State Key Lab of Autonomous Intelligent Unmanned Systems, Beijing Institute of Technology, Beijing 100081, China（自主智能无人系统国家重点实验室，北京理工大学，北京100081，中国）；Zhongguancun Academy, Beijing 100094, China（中关村学院，北京100094，中国）；School of Computer Science and Technology, Tongji University（同济大学计算机科学与技术学院）；Department of Mechanical and Energy Engineering, Southern University of Science and Technology（南方科技大学机械与能源工程系）；Harbin Institute of Technology（哈尔滨工业大学）
A local map module is often implemented in modern VO/VSLAM systems to improve data association and pose estimation. Conventionally, the local map contents are determined by co-visibility. While co-visibility is cheap to establish, it utilizes the relatively-weak temporal prior (i.e. seen before, likely to be seen now), therefore admitting more features into the local map than necessary. This paper describes an enhancement to co-visibility local map building by incorporating a strong appearance prior, which leads to a more compact local map and latency reduction in downstream data association. …
Autonomous detection of lane markers improves road safety, and purely visual tracking is desirable for widespread vehicle compatibility and reducing sensor intrusion, cost, and energy consumption. However, visual approaches are often ineffective because of a number of factors; e.g., occlusion, poor weather conditions, and paint wear-off. We present an approach to enhance lane marker appearance for assisted and autonomous driving, particularly under poor visibility. Our method, named SafeDrive, attempts to improve visual lane detection approaches in drastically degraded visual conditions. SafeD…
In 2021, Japan recorded more than three times as much snowfall as usual, so road user maybe come across dangerous situation. The poor visibility caused by snow triggers traffic accidents. For example, 2021 January 19, due to the dry snow and the strong wind speed of 27 m / s, blizzards occurred and the outlook has been ineffective. Because of the whiteout phenomenon, multiple accidents with 17 casualties occurred, and 134 vehicles were stacked up for 10 hours over 1 km. At the night time zone, the temperature drops and the road surface tends to freeze. CCTV images on the road surface have the …
RoadRunner -- Learning Traversability Estimation for Autonomous Off-road Driving
Jet Propulsion Laboratory (JPL)（喷气推进实验室）；California Institute of Technology (Caltech)（加州理工学院）；Swiss Federal Institute of Technology (ETH Zürich)（苏黎世联邦理工学院）
Finnish Geospatial Research Institute FGI, National Land Survey of Finland（芬兰国家土地调查局芬兰地理空间研究所（FGI））；Aalto University School of Science（阿尔托大学理学院）
Autonomous driving is challenging in adverse road and weather conditions in which there might not be lane lines, the road might be covered in snow and the visibility might be poor. We extend the previous work on end-to-end learning for autonomous steering to operate in these adverse real-life conditions with multimodal data. We collected 28 hours of driving data in several road and weather conditions and trained convolutional neural networks to predict the car steering wheel angle from front-facing color camera images and lidar range and reflectance data. We compared the CNN model performances…
EventSSEG: Event-driven Self-Supervised Segmentation with Probabilistic Attention
Defence Research and Development Organization, Bangalore and Indian Institute of Science, Bangalore（国防研究与发展组织、班加罗尔和印度科学学院，班加罗尔）；Department of Electronics Systems Engineering, Indian Institute of Science, Bangalore（电子系统工程系，印度科学学院，班加罗尔）
