Dhiraj Gulati

CHROMOSOME: a run-time environment for plug & play-capable embedded real-time systems

Developers of embedded systems are increasingly facing requirements concerning adaptivity. The term adaptivity covers several different aspects. In this paper, we present an innovative open-source run-time system that enables adding and removing applications and adapting to changes in the hardware topology while still guaranteeing traditional requirements of embedded systems such as real-time or safety. The system uses a data-centric approach to achieve a modular application design and to enable the interaction of application components created by independent developers. Using a requirements-centric approach, the system can reserve the resources such as processor time, memory or network bandwidth required by application components. New applications can only be added if enough resources are available. The paper details these concepts and presents the architecture of the run-time system.

Cooperative vehicle-infrastructure localization based on the symmetric measurement equation filter

Precise and accurate localization is important for safe autonomous driving. Given a traffic scenario which has multiple vehicles equipped with internal sensors for self-localization, and external sensors from the infrastructure for vehicle localization, vehicle-infrastructure communication can be used to improve the accuracy and precision of localization. However, as the number of vehicles in a scenario increases, associating measurement data with the correct source becomes increasingly challenging. We propose a solution utilizing the symmetric measurement equation filter (SME) for cooperative localization to address data association issue, as it does not require an enumeration of measurement-to-target associations. The principal idea is to define a symmetrical transformation which maps measurements to a homogeneous function, thereby effectively addressing several challenges in vehicle-infrastructure scenarios such as data association, bandwidth limitations and registration/configuration of the external sensor. To the best of our knowledge, the proposed solution is among the first to address all these issues of cooperative localization simultaneously, by utilizing the topology information of the vehicles.

Graph-Based Cooperative Localization Using Symmetric Measurement Equations

Safety is one of the critical challenges for a semi or fully automated driving assistance systems. One of the key parameters for a safe automated driving assistance system is precise localization of the self and surrounding vehicles. Our previous work demonstrated the use of Symmetric Measurement Equations (SME) in a Factor Graph framework and exploited the use of sensor located outside the vehicle. In this paper we present a new approach which not only performs above mentioned cooperative vehicle infrastructure localization but also uses Dedicated Short Range Communication (DSRC). This work goes further in the direction of a complete V2X solution not only involving the infrastructure sensor but also the neighbouring vehicles. DSRC has been increasingly incorporated in all the the modern vehicles. Better state estimation is achieved by formulating the range information from DSRC as a new DSRC Range Factor in the Factor Graph. Simulations indicate better performance over the previously proposed approach of only using SME in the Factor Graph, thereby progressing a step further towards safe automated driving assistance systems.

Vehicle infrastructure cooperative localization using Factor Graphs

Highly assisted and Autonomous Driving is dependent on the accurate localization of both the vehicle and other targets within the environment. With increasing traffic on roads and wider proliferation of low cost sensors, a vehicle-infrastructure cooperative localization scenario can provide improved performance over traditional mono-platform localization. The paper highlights the various challenges in the process and proposes a solution based on Factor Graphs which utilizes the concept of topology of vehicles. A Factor Graph represents probabilistic graphical model as a bipartite graph. It is used to add the inter-vehicle distance as constraints while localizing the vehicle. The proposed solution is easily scalable for many vehicles without increasing the execution complexity. Finally simulation indicates that incorporating the topology information as a state estimate can improve performance over the traditional Kalman Filter approach.

Graph based cooperative localization using symmetric measurement equations and dedicated short range communication

Safety is one of the critical challenges for a semi or fully automated driving assistance systems. One of the key parameters for a safe automated driving assistance system is precise localization of the self and surrounding vehicles. Our previous work demonstrated the use of Symmetric Measurement Equations (SME) in a Factor Graph framework and exploited the use of sensor located outside the vehicle. In this paper we present a new approach which not only performs above mentioned cooperative vehicle infrastructure localization but also uses Dedicated Short Range Communication (DSRC). This work goes further in the direction of a complete V2X solution not only involving the infrastructure sensor but also the neighbouring vehicles. DSRC has been increasingly incorporated in all the the modern vehicles. Better state estimation is achieved by formulating the range information from DSRC as a new DSRC Range Factor in the Factor Graph. Simulations indicate better performance over the previously proposed approach of only using SME in the Factor Graph, thereby progressing a step further towards safe automated driving assistance systems.

Robust cooperative localization in a dynamic environment using factor graphs and probability data association filter

Autonomous vehicles operating in dynamic environments rely on precise localization. In this paper we present a novel approach for cooperative localization of vehicular systems and an infrastructure RADAR which is resilient against outliers generated from the RADAR. The problem of cooperative localization is represented as a factor graph, where interrelated topologies (including that of outliers) are added as constraint factor between vehicle states. Corresponding probabilities for multiple topologies between states of the two vehicles are calculated using the Probability Data Association Filter and assigned to the respective edges in the graph. Simulation results indicate that this technique has significant benefits in the context of improving the resilience against outliers while optimizing joint state estimates. The methodology presented in this paper has the potential to provide a robust and flexible framework for cooperative localization in the presence of clutter, obscuration and targets entering and leaving the field of view.

Towards Dynamic and Flexible Sensor Fusion for Automotive Applications

In this paper we describe the concept of the data fusion and system architecture to be implemented in the collaborative research project Smart Adaptive Data Aggregation (SADA). The objective of SADA is to develop technologies that enable linking data from distributed mobile on-board sensors (on vehicles) with data from previously unknown stationary (e.g., infrastructure) or mobile sensors (e.g., other vehicles, smart devices). Data not only can be processed locally in the car, but also can be collected in a central backend, to allow machine learning based inference of additional information (enabling so-called crowd sensing). Ideally, crowd sensing might provide virtual sensors that could be used in the SADA fusion process.

Data association — solution or avoidance: Evaluation of a filter based on RFS framework and factor graphs with SME

Data or measurement-to-track association is an integral and expensive part of any solution performing multi-target multi-sensor Cooperative Localization (CL) for better state estimation. Various performance evaluations have been performed between various state-of-the-art solutions, but they have been often limited within same family of algorithms. However, there exist solutions which avoid the task of data association to perform the CL in a multi-target multi-sensor environment. Factor Graphs using Symmetric Measurement Equations (SMEs) factor is one such solution. In this paper we compare and contrast the state estimation using state-of-the-art Random Finite Set (RFS) approach and using a Factor Graph solution with SMEs. For a RFS we use multi-sensor multi-object with the Generalized Labeled Multi-Bernoulli (GLMB) Filter. These two solution use conceptually different approaches, GLMB Filter solves the data association implicitly, but Factor Graph based solution avoids the task altogether. Simulations present an interesting results where for simple scenarios implemented GLMB filter performs efficiently. But the performance of GLMB Filter degrades faster than Factor Graphs using SMEs when the error in the sensors increase.

Graph based vehicle infrastructure cooperative localization

This paper presents a novel and an improved approach for estimating the position of a vehicle using vehicle-infrastructure cooperative localization. In our previous work we presented a Factor Graph based solution which added the topology (inter-vehicle distance) as a constraint while localizing the vehicle using data from sensors from both inside and outside the vehicle. This paper extends the work by reducing the error in calculating the precision of the position by almost 27% in the best case and lowering the computational time by at least 50% over our previously proposed solution. This is achieved by modifying current topology constraints to be also dependent on the previous state estimate. The proposed solution remains scalable for many vehicles without increasing the execution complexity. Finally, simulations indicate that incorporating the new topology information via Factor Graphs can improve performance over the traditional, state of the art, Kalman Filter approach.

Joint bias estimation and localization in factor graph

This paper describes a new approach for cooperative localization by using both internal and external sensors. In contrast to the state-of-the-art methods, the proposed approach analyses the statistical properties of the systematic error during the transformation phase. A factor graph is formulated which jointly estimates both the biases and the locations. The proposed approach is evaluated by using simulated data from odometry, GPS and radar measurements. The experiment demonstrates excellent performance of the proposed approach in comparison to traditional techniques.