Advanced driver assistance systems (ADAS) and automated driving functions are becoming integral to modern vehicles. Ensuring their safety and reliability requires validating their operation within well-defined Operational Design Domains (ODD). Monitoring ODD compliance is crucial to determine when these functions can operate safely. This paper presents a systematic approach to ODD monitoring using a formalized, machine-readable ODD description and fuzzy logic. The method evaluates compliance and provides explanations for non-compliance. The approach introduces a two-level hierarchical ODD representation, a membership score quantifying compliance, and an explanation mechanism for identifying the primary factors contributing to non-compliance. The monitoring results are integrated into a Conditional Activation Control System (CACS), which governs function activation based on ODD compliance. The proposed system was implemented within a production vehicle and validated using real-world data, demonstrating its feasibility for deployment. By enabling clear, real-time assessments of ODD adherence, this approach supports safer and more reliable automated driving, promoting user confidence and regulatory compliance.

Path planning algorithms for autonomous vehicles need to account for safety and comfort, more so, in scenarios where the possibility of casualties are higher due to increased traffic frequency and limited visibility. In this paper, we discuss the idea of a virtual obstacle deployed at occluded scenarios to avoid a potential collision or severe deceleration of the ego-vehicle. Urban scenarios like intersections, roundabout and merging are experimented. Results of simulating the integration of virtual obstacle with the trajectory planning algorithm, are analyzed in detail comparing speed and acceleration profiles.

Shared control is a situation where humans and machines carry out a task in a cooperative way, and it is a popular approach to facilitate control and communication between humans and intelligent systems. Automated driving vehicles that share control with human drivers have been shown to acts better than fully automated systems and manual driving. However, there is little consensus in guidelines for the design and definition of shared control, some researchers assist humans using haptic feedback, and others use an optimization process and many other works. In this paper, a non-haptic shared control is applied to navigate an intelligent vehicle based on two inputs one by a human driver and the other obtained by an autonomous algorithm. Where, Non-Cooperative Game Theory is applied to formulate the final input of the vehicle in the form of linear velocity and steering wheel angle; where a joystick is used for manipulating human inputs and a computer vision technology “Visual Servoing” is implemented to command the autonomous driving inputs, after calculating the fusion coefficient value, which is determined by a function dependent on the admissibility of inputs and the similarity between them. The final navigation decision sent to the intelligent vehicle is the output of the fusion system. Noting that, it has the only right to command the driving inputs to the vehicle. Finally, simulation results are presented with the help of SCANeR Studio and MATLAB/SIMULINK Simulators.

Occupancy grids are common tools used in robotics to represent the robot environment, and that may be used to plan trajectories, select additional measurements to acquire, etc. However, deriving information about those occupancy grids from sensor measurements often induce a lot of uncertainty, especially for grid elements that correspond to occluded or far away area from the robot. This means that occupancy information may be quite uncertain and imprecise at some places, while being very accurate at others. Modelling finely this occupancy information is essential to decide the optimal action the robot should take, but a refined modelling of uncertainty often implies a higher computational cost, a prohibitive feature for real-time applications. In this paper, we introduce the notion of credal occupancy grids, using the very general theory of imprecise probabilities to model occupancy uncertainty. We also show how one can perform efficient, real-time inferences with such a model, and show a use-case applying the model to an autonomous vehicle trajectory planning problem.

Lane detection plays a crucial role in any autonomous driving system. Currently commercialized vehicles offer lane keep assist and lane departure warning via integrated smart cameras, deployed for road markings detection. These sensors alone, however, do not generally ensure adequate performance for higher autonomy levels. In this paper, a multi-sensor tracking approach for generic lane boundaries is proposed. This solution is based on well-established filtering techniques and supports a flexible clothoid spline representation. It relies on fine-tuned measurement models, tailored on collected data from both off-the-shelf and prototype smart sensors. The implementation takes into account real-time constraints and ADAS ECUs scarcity of resources. The result is finally validated against lane-level ground truth and experimental data acquisitions.

Driving is a combination of continuous mental risk assessment, sensory awareness, and judgment, all adapting to extremely variable surrounding conditions. Several research projects are working on autonomous vehicle to robotize this complex task. The work presented in this paper focuses on reactive local trajectory planning in uncertain environment. The environment uncertainty is one of the challenges that we face in trajectory planning. For autonomous vehicle, to be efficient, they need to be able to deal with this kind of uncertainty. In this paper, we show that the theory of belief functions with its ability to distinguish between different types of uncertainty is able to provide significant advantages in the context of trajectory planning. Using the belief functions, we build evidential grid that represents the surrounding environment. To plan a local trajectory, we generate a set of clothoid tentacles in the egocentred reference frame related to the ego-vehicle. Those tentacles represent possible trajectories that consider the current dynamical state of the vehicle and make a smooth variation in the vehicle dynamic variables. Once the representation of the environment and the possible trajectories are generated, an evaluation of each trajectory is carried out according to several criteria and the choice of the trajectory is made using the decision formalism of Markov decision process. To demonstrate the effectiveness of our evidential approach, we apply it to scenarios where ego-vehicle has to make decision in uncertain dynamical environments, using a driving simulator (SCANeR™ Studio).

In traditional LIDAR processing pipelines, a point-cloud is split into clusters, or objects, which are classified afterwards. This supposes that all the objects obtained by clustering belong to one of the classes that the classifier can recognize, which is hard to guarantee in practice. We thus propose an evidential end-to-end deep neural network to classify LIDAR objects. The system is capable of classifying ambiguous and incoherent objects as unknown, while only having been trained on vehicles and vulnerable road users. This is achieved thanks to an evidential reformulation of generalized logistic regression classifiers, and an online filtering strategy based on statistical assumptions. The training and testing were realized on LIDAR objects which were labelled in a semi-automatic fashion, and collected in different situations thanks to an autonomous driving and perception platform.

In Demspter-Shafer theory the discounting operation can be used to weak the belief according to the reliability of the source of information. This is usually done by modifying basic belief assignment also called mass function. The canonical decomposition- well adapted for some combination rules- is an other way to represent the belief of an agent. We propose in this paper to focus on discounting methods that are directly applied to the canonical decomposition and we compare them to methods previously studied in the literature. Then we propose an equation that implements the discounting on canonical decomposition strictly equivalent to the classical discounting method. This approach reduces the computation cost significatively. Finally, we illustrate the validity of the method by demonstrating the convergence of the distributed data fusion algorithm using an operator based on the cautious rule and discounting. using an application on a distributed data fusion algorithm, we demonstrate the convergence when the cautious operator is used.

The work presented in this paper focuses on reactive local trajectory planning which plays an essential role for future autonomous vehicles. The challenge is to avoid obstacles in respect to road rules while following a global reference trajectory. The planning approach used in this work is the method of clothoid tentacles generated in the egocentered reference frame related to the vehicle. Generated tentacles in a egocentered grid represent feasible trajectories by the vehicle, and in order to choose the right one, we formulate the problem as a Markov Decision Process.

The Theory of Belief Functions is a formal framework for reasoning with uncertainty that is well suited for representing unreliable information and weak states of knowledge. In a previous work, a distributed algorithm for computing data fusion on-the-fly has been introduced, avoiding gathering the data on a single node before computation. In this paper, we present an experimental study of its properties. This algorithm is self-stabilizing and runs on unreliable message passing networks. It converges in finite time whatever is the initialization of the system and for any unknown topology. First we explain the algorithm implementation on an unreliable message passing environment and we implement a simple use-case. Then, by experimenting with this distributed application on a realistic network emulator, we show its interest for enforcing local confidence using close nodes, saving bandwidth and warning dangers. Moreover, we focus on the interesting connections between the data fusion operator and the self-stabilizing properties and we highlight the importance of the discounting.

One of the main ideas in the area of intelligent transport systems is to use all possible information coming from vehicles and infrastructure, in order to make the system “smarter” and avoid potentially dangerous situations-collisions, accidents, bottleneck, etc. However, data are sometimes unreliable due to source and communication network quality, leading vehicles or even the whole system to wrong decisions. We present a generic method for detecting dangerous events on the road. To support unreliable data sources, it uses distributed data fusion. Moreover, to deal with network failures, it relies on a self-stabilizing generic distributed algorithm. Our method mixes measurements obtained from vehicle onboard sensors, as well as wireless sensors placed close to the road and connected to road side units. Each vehicle computes how confident it is about a potential dangerous event using both local and remote data. To evaluate our approach, we implemented it using a specific hardware and software platform. Moreover, we instantiated a simple, yet efficient application to detect icy roads, based on temperature measurements. Thanks to both in-lab and actual on-the-road experiments, we demonstrate the possibility to deduce proper results from unreliable data and, consequently, the correctness and usefulness of our approach.

Understanding driver behaviors is an important need for the Advanced Driver Assistance Systems. In particular, the pedestrian detection systems become extremely distracting and annoying when they inform the driver with unnecessary warning messages. In this paper, we propose to study the driver behaviors whenever a pedestrian appears in front of the vehicle. A method based on the driving actions and the Hidden Markov Model (HMM) algorithm is developed to classify the driver awareness of pedestrian and the driver unawareness of pedestrian. The method is successfully validated using the collected data from the experiments that are conducted on a driving simulator. Furthermore, two simple methods based on the static parameters such as the Time-To-Collision and the Required Deceleration Parameter are also applied to our problem and are compared to the proposed method. The result shows a significant improvement of the HMM-based method compared to the simple ones.

Occupancy grids have shown interesting properties to model the environment for intelligent vehicles perception. In this paper, we present a novel approach to build 2D occupancy grid maps with stereo-vision. Our approach proposes a fitted sensor model based on the disparity space to interpret the stereo-vision information onto an occupancy grid map. The evidential model deals with sensor uncertainties by using Dempster-Shafer theory. Our approach exploits the U-disparity space to model the obstacle information and the V-disparity space to model the road space information. The fusion of these two sources of complementary information results to an enhanced environmental model. In a first experimental data set, results based on real road data and comparisons with Lidar grids show that the proposed evidential sensor model can model efficiently the environment. In a second one, the mapping of a road environment is reported to show the performance of the proposed model with another stereo-vision system.

For the development of driving assistance systems and autonomous vehicles, a reference perception equipment including navigable space determination and obstacles detection is a key issue. The Velodyne sensor which provides high definition and omnidirectional information can be used for this purpose. Nevertheless, when scanning around the vehicle, uncertainty necessarily arises due to unperceived areas and noisy measurements. This paper proposes an inverse evidential model for the Velodyne in order to exploit its measurements in a 2D occupancy grid mapping framework. The evidential sensor model interprets the data acquired from the Velodyne and successively maps it to a Carthesian evidential grid using a fusion process based on the least commitment principle to guarantee information integrity. Experimental results prove that this approach can handle efficiently the uncertainties of the sensor and thus a highly reliable local reference map near the vehicle can be built for every timestamped perception system that needs evaluation or calibration.

In this paper, we present a distributed approach to build a dynamic map in the context of VANets (Vehicular Ad hoc Networks). It is based on the principle of cooperative perception where vehicles work as a team in order to extend their field of view. Each vehicle is equipped with sensors allowing it to detect its environment and to build its map, denoted by local map. It receives messages from other vehicles containing mobile objects detected in their surroundings. The algorithm of distributed dynamic map builds a map of the dynamic environment including objects in the sensor's field of view as well as those sent by other vehicles. This algorithm is developed under the belief functions framework. The implementation of such an application is complex and needs many treatments: temporal and spatial alignment, object association, fusion of messages and data dissemination. This approach has been validated by simulation on scenario involving several vehicles in traffic situation.

Object association is a crucial step in target tracking and data fusion applications. This task can be formalized as the search for a relation between two sets (e.g., a sets of tracks and a set of observations) in such a way that each object in one set is matched with at most one object in the other set. In this paper, this problem is tackled using the formalism of belief functions. Evidence about the possible association of each object pair, usually obtained by comparing the values of some attributes, is modeled by a Dempster-Shafer mass function defined in the frame of all possible relations. These mass functions are combined using Dempster's rule, and the relation with maximal plausibility is found by solving an integer linear programming problem. This problem is shown to be equivalent to a linear assignment problem, which can be solved in polynomial time using, for example, the Hungarian algorithm. This method is demonstrated using simulated and real data. The 3-D extension of this problem (with three object sets) is also formalized and is shown to be NP-Hard.

Predicting other traffic participants trajectories is a crucial task for an autonomous vehicle, in order to avoid collisions on its planned trajectory. It is also necessary for many Advanced Driver Assistance Systems, where the ego-vehicle's trajectory has to be predicted too. Even if trajectory prediction is not a deterministic task, it is possible to point out the most likely trajectory. This paper presents a new trajectory prediction method which combines a trajectory prediction based on Constant Yaw Rate and Acceleration motion model and a trajectory prediction based on maneuver recognition. It takes benefit on the accuracy of both predictions respectively a short-term and long-term. The defined Maneuver Recognition Module selects the current maneuver from a predefined set by comparing the center lines of the road's lanes to a local curvilinear model of the path of the vehicle. The overall approach was tested on prerecorded human real driving data and results show that the Maneuver Recognition Module has a high success rate and that the final trajectory prediction has a better accuracy.

Taking into account the drivers' state is a major challenge for designing new advanced driver assistance systems. In this paper we present a driver assistance system strongly coupled to the user. DAARIA1stands for Driver Assistance by Augmented Reality for Intelligent Automobile. It is an augmented reality interface powered by several sensors. The detection has two goals: one is the position of obstacles and the quantification of the danger represented by them. The other is the driver's behavior. A suitable visualization metaphor allows the driver to perceive at any time the location of the relevant hazards while keeping his eyes on the road. First results show that our method could be applied to a vehicle but also to aerospace, fluvial or maritime navigation.

Recent and future driver assistance systems use more and more sensors, that have individual tracking modules. For target tracking, it becomes necessary to find techniques to manage as simply as possible the use of a great number of independent and heterogeneous sensors, at the different stages of the process. This paper presents a modular highlevel track-fusion architecture for a multisensor environment. This architecture allows the variation of the number and the types of the used sensors with no major change in the tracking algorithm. The paper also tackles the multisensor track-to-track association issue with a new algorithm based on a particular track-to-track distance computation. An example of target tracking method is shown to make use of the proposed architecture and the track-to-track association algorithm.

The characterization in real-time of the drivable space in front of the vehicle is a key issue for safe autonomous navigation or driving assistance. This paper presents a method that uses a lidar (a multilayer laser scanner) integrated in the front bumper of an automotive vehicle. A grid processing is first applied to detect and localize objects in the immediate environment after having compensated the movement of the vehicle. Accurate map information is then introduced in the perception scheme to refine the characterization of the drivable space. The paper details the different processing stages necessary to implement this method and presents the design of the system that has been prototyped on board an experimental vehicle. We report real experiments carried out in challenging urban environments to illustrate the performance of this approach which has been evaluated thanks to a precise retro-projection of the estimated drivable space in a wide-angle scene camera.

In urban environments, moving obstacles detection and free space determination are key issues for driving assistance systems and autonomous vehicles. When using lidar sensors scanning in front of the vehicle, uncertainty arises from ignorance and errors. Ignorance is due to the perception of new areas and errors come from imprecise pose estimation and noisy measurements. Complexity is also increased when the lidar provides multi-echo and multi-layer information. This paper presents an occupancy grid framework that has been designed to manage these different sources of uncertainty. A way to address this problem is to use grids projected onto the road surface in global and local frames. The global one generates the mapping and the local one is used to deal with moving objects. A credibilist approach is used to model the sensor information and to do a global fusion with the world-fixed map. Outdoor experimental results carried out with a precise positioning system show that such a perception strategy increases significantly the performance compared to a standard approach.

Advanced Driving Assistance Systems exploit exteroceptive sensors to help the driver in perceiving the dynamic environment, like other vehicles or pedestrians. This paper proposes an original approach to deal with this perception challenge in urban environments. The method detects mobile objects motions using grids elaborated thanks to a lidar range scanner and an enhanced map of the drivable space. The data fusion is performed using the Dempster-Shafer theory which provides an interesting framework particularly well adapted to manage the uncertainties of the sensors. By analyzing conflicting information, objects movements can be efficiently characterized. This formalism provides also the interesting possibility to introduce decay factors that are useful for forgetting old information. Experimental results obtained with an IBEO Alasca and an Applanix positioning system show that such a perception strategy can be effective compared to deterministic accumulation strategies.

In urban environments, detection of moving obstacles and free space determination are key issues for driving assistance systems or autonomous vehicles. This paper presents a lidar-based perception system for passenger-cars, able to do simultaneously mapping and moving obstacles detection. Nowadays, many lidars provide multi-layer and multi-echo measurements. A smart way to handle this multi-modality is to use grids projected on the road surface in both global and local frames. The global one generates the mapping and the local is used to deal with moving objects. An approach based on both positive and negative accumulation has been developed to address the remnant problem of quickly moving obstacles. This method is also well suited for multi-layer and multi-echo sensors. Experimental results carried out with an IBEO Alasca and an Applanix positioning system show the performance of such a perception strategy.

Reliable obstacle detection and localization is a key issue for driving assistance systems particularly in urban environments. In this article, a multi-modal perception approach is studied in order to enhance vehicle localization and dynamic objects tracking, in a world-centric map. 3D ego-localization is done by merging a stereo vision system and proprioceptive information coming from vehicle sensors. Mobile objects are detected using a multi-layer lidar that is simultaneously used to characterize a zone of interest in order to reduce the complexity of the perception process. Objects localization and tracking is then performed in the fixed frame which simplifies the scene analysis and understanding. Real experimental results are reported to evaluate the performance of the multi-modal system.

Most signal and fusion processings consider synchronous data (acquired at the same time) and neglect small time differences. In dynamic situations, the time delay between two supposed synchronous data can imply errors. These errors can be a drawback in some applications, where data are required at a same exact time. This paper proposes to correct the error produced by timestamping error in sensor data acquisition. An example of such data is shown for laser range scanners that acquire data at a fixed frequency. All values are estimated at a same specified time. Experimentations have been carried out, with a laser range scanner (SICK LMS 291) which scans 180 degrees horizontal rows at frequency of 75 Hz. It has been placed at the back of an instrumented car, and an error correction up to 40 cm has been observed

This paper describes a solution for online timestamping in a distributed architecture embedded in an experimental vehicle. Interval timestamping is used, taking into consideration sensor latency, transmission delay and clock granularity. This solution does not change local system clocks, so that the network configuration can change without affecting timestamping precision. All nodes of the network are connected via a synchronous bus network (here, the FireWire, IEEE 1394). The bus clock is used to estimate the drift of all computer clocks and to exchange data timestamps with high precision. Experimental simulations show the advantages of this solution. The method is well adapted to dynamic applications, where data timestamping is important for real time considerations. An application in the field of intelligent vehicles is then described.

We present the development of a multi-objects matching algorithm with ambiguity removal entering into the design of a dynamic perception system for intelligent vehicles. The originality of this system lies in the use of theories such as fuzzy mathematics and belief theory which allow the handling of inaccurate us well as uncertain information Moreover, these theories allow both numeric and symbolic data fusion. We started from the hypothesis that we have some sensors providing redundant information in time. We develop in the article the problem of matching between the prediction (known objects) and the perception result (perceived objects). This makes it possible to update a dynamic environment map for a vehicle. The belief theory will enable us to quantify association belief on each perceived and known object. Some conflicts can appear in the case of object appearance or disappearance or in the case of a bad perception or a confused situation. These conflicts are removed or solved using an assignment algorithm, giving a solution called the "best" and so ensuring multi-objects tracking.