A Multi-level Cooperative Perception Scheme for Autonomous Vehicles (original) (raw)
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Cooperative Perception Algorithms for Networked Intelligent Vehicles
2017
The degree of intelligence built-in in today's vehicles in constantly on the rise. The vehicles are being equipped with sensors, with the goal to estimate the state of the vehicle and the environment surrounding it. Intelligent algorithms that process the sensory data can give their output at different levels, ranging from simple warnings, over evasive maneuvers (such as emergency braking), to complete autonomy. While it has been demonstrated that autonomous vehicles can rely solely on their on-board sensors, their performance can be optimized through cooperation with other road vehicles. Information coming from infrastructure can be fused in as well. This is where the communication between vehicles, as well as between vehicles and the infrastructure, comes into play. The main benefits of cooperation include larger coverage and extended situational awareness through sharing sensor data and vehicle intentions (trajectories). In this thesis, we address the cooperative perception p...
A Cooperative Perception Environment for Traffic Operations and Control
arXiv (Cornell University), 2022
Existing data collection methods for traffic operations and control usually rely on infrastructurebased loop detectors or probe vehicle trajectories. Connected and automated vehicles (CAVs) not only can report data about themselves but also can provide the status of all detected surrounding vehicles. Integration of perception data from multiple CAVs as well as infrastructure sensors (e.g., LiDAR) can provide richer information even under a very low penetration rate. This paper aims to develop a cooperative data collection system, which integrates Lidar point cloud data from both infrastructure and CAVs to create a cooperative perception environment for various transportation applications. The state-of-the-art 3D detection models are applied to detect vehicles in the merged point cloud. We test the proposed cooperative perception environment with the max pressure adaptive signal control model in a co-simulation platform with CARLA and SUMO. Results show that very low penetration rates of CAV plus an infrastructure sensor are sufficient to achieve comparable performance with 30% or higher penetration rates of connected vehicles (CV). We also show the equivalent CV penetration rate (E-CVPR) under different CAV penetration rates to demonstrate the data collection efficiency of the cooperative perception environment.
Survey on Cooperative Perception in an Automotive Context
IEEE Transactions on Intelligent Transportation Systems, 2022
The idea of cooperation has been introduced to selfdriving cars about a decade ago with the aim to reduce the occlusion caused by other users or the scene. More recently, the research efforts turned toward cooperative infrastructure bringing a new kind of the point of view as well as more processing power. This paper lies in this new field providing a survey that addresses the cooperative environment. We provide an overview of the architectures available to create such a system as well as the challenges introduced by the cooperation. Later, we review the main blocks involved in the perception: localization, object detection & tracking, map generation. Each block is reviewed under the prism of cooperation. We also provide a Strengths, Weaknesses, Opportunities, and Threats (SWOT) analysis of the cooperative perception as well as a list of related scenarios alongside experimentations. Finally, we list some related datasets before concluding our paper, underlining the perspectives for further works.
Cooperative Perception: Application in the Context of Outdoor Intelligent Vehicle Systems
Spécialité "Informatique temps réel, Robotique, Automatique" Directeur de thèse : Fawzi NASHASHIBI T H È S E Jury M. Roland CHAPUIS, Professeur Polytech Clermont-Ferrand Rapporteur M. Philippe BONNIFAIT Professeur UTC Compiegne Rapporteur M. Christian LAUGIER DR1/Thèse d'Etat INRIA Rhône-Alpes Président M. Bruno STEUX Docteur Mines Paristech Examinateur M. Michel PARENT
Sensors
Cooperative perception, or collective perception (CP), is an emerging and promising technology for intelligent transportation systems (ITS). It enables an ITS station (ITS-S) to share its local perception information with others by means of vehicle-to-X (V2X) communication, thereby achieving improved efficiency and safety in road transportation. In this paper, we present our recent progress on the development of a connected and automated vehicle (CAV) and intelligent roadside unit (IRSU). The main contribution of the work lies in investigating and demonstrating the use of CP service within intelligent infrastructure to improve awareness of vulnerable road users (VRU) and thus safety for CAVs in various traffic scenarios. We demonstrate in experiments that a connected vehicle (CV) can “see” a pedestrian around the corners. More importantly, we demonstrate how CAVs can autonomously and safely interact with walking and running pedestrians, relying only on the CP information from the IR...
2013 IEEE/RSJ International Conference on Intelligent Robots and Systems, 2013
In this paper, we attempt to develop a reusable framework of cooperative perception for vehicle control on the road that can extend perception range beyond line-ofsight and beyond field-of-view. For this goal, the following problems are addressed: map merging, vehicle identification, sensor multi-modality, impact of communications, and impact on path planning. We provide experimental results using a self-driving vehicle and manned vehicles equipped with the cooperative perception systems that we propose and implement.
Towards a cooperating autonomous car
2002
The car of the future will be equipped with a large number of sensors, ranging from position and speed sensors to sensors indicating the presence of obstacles or indicating the road and weather conditions. All these sensors will be used by the car to perceive the reality and actuate according to it (eg braking, deviating from obstacles, horning). Furthermore, cars will communicate with other cars or entities in its proximity, over a wireless link, to achieve cooperation and coordination in certain occasions [1].
A Grid-Based Framework for Collective Perception in Autonomous Vehicles
Sensors, 2021
Today, perception solutions for Automated Vehicles rely on sensors on board the vehicle, which are limited by the line of sight and occlusions caused by any other elements on the road. As an alternative, Vehicle-to-Everything (V2X) communications allow vehicles to cooperate and enhance their perception capabilities. Besides announcing its own presence and intentions, services such as Collective Perception (CPS) aim to share information about perceived objects as a high-level description. This work proposes a perception framework for fusing information from on-board sensors and data received via CPS messages (CPM). To that end, the environment is modeled using an occupancy grid where occupied, and free and uncertain space is considered. For each sensor, including V2X, independent grids are calculated from sensor measurements and uncertainties and then fused in terms of both occupancy and confidence. Moreover, the implementation of a Particle Filter allows the evolution of cell occupa...
Integrating Shared Information into the Sensorial Mapping of Connected and Autonomous Vehicles
13th International Conference on Agents and Artificial Intelligence (ICAART 2021), Feb 2021, Lisbonne (virtuel), Portugal. pp.454-461, 2021
A connected and autonomous vehicle (CAV) needs to dynamically maintain a map of its environment. Even if the self-positioning and relative localization of static objects (roads, signs, poles, guard-rails, buildings, etc.) can be done with great precision thanks to the help of hd-maps, the detection of the dynamic objects on the scene (other vehicles, bicycles, pedestrians, animals, casual objects, etc.) must be made by the CAV itself based on the interpretation of its low-level sensors (radars, lidars, cameras, etc.). In addition to the need of representing those moving objects around it, the CAV (seen as an agent immersed in that traffic environment) must identify them and understand their behavior in order to anticipate their expected trajectories. The accuracy and completeness of this real-time map, necessary for safely planning its own maneuvers, can be improved by incorporating the information transmitted by other vehicles or entities within the surrounding neighborhood through V2X communications. The implementation of this cooperative perception can be seen as the last phase of perception fusion, after the in-vehicle signals (coming from its diverse sensors) have already been combined. In this position paper, we approach the problem of creating a coherent map of objects by selecting relevant information sent by the neighbor agents. This task requires correctly identifying the position of other communicant agents, based both on the own sensory perception and on the received information, and then correcting and completing the map of perceived objects with the communicated ones. For doing so, the precision and confidence on each information must be taken into account, as well as the trust and latency associated with each source. The broad objective is to model and simulate a fleet of vehicles with different levels of autonomy and cooperation, based on a multi-agent architecture, in order to study and improve road safety, traffic efficiency, and passenger comfort. In the paper, the problem is stated, a brief survey of the state-of-the-art on related topics is given, and the sketch of a solution is proposed.
Multiagent Information Fusion for Connected Driving: A Review
IEEE Access, 2022
This paper reviews the state-of-the-art multi-sensor fusion approaches applicable in the next-generation intelligent transportation systems where connected vehicles are cooperatively driven for maximum safety and efficiency. The review finds out that complementary sensor fusion in a time-varying distributed network is required, and for such applications, the state-of-the-art is sensor fusion in the random finite set filtering framework. The fundamental bases of random finite set filters are reviewed with more elaboration on a particular filter called the Labeled Multi-Bernoulli filter. An information-theoretic approach for data fusion based on minimizing information divergence between statistical densities is presented, along with how different divergence functions can be used for sensor fusion. Different approaches are evaluated for their tracking performance and computational cost in a realistic simulation scenario. Their advantages, and disadvantages in the context of real-time implementation in a connected driving scenario are discussed. INDEX TERMS Random finite sets, intelligent transport systems, multi-object tracking, information fusion.