Intention-Aware Risk Estimation for General Traffic Situations, and Application to Intersection Safety

This work tackles the risk estimation problem from a new perspective: a framework is proposed for reasoning about traffic situations and collision risk at a semantic level, while classic approaches typically reason at a trajectory level. Risk is assessed by estimating the intentions of drivers and detecting conflicts between them, rather than by predicting the future trajectories of the vehicles and detecting collisions between them. More specifically, dangerous situations are identified by comparing what drivers intend to do with what they are expected to do according to the traffic rules. The reasoning about intentions and expectations is performed in a probabilistic manner, in order to take into account sensor uncertainties and interpretation ambiguities. This framework can in theory be applied to any type of traffic situation; here we present its application to the specific case of road intersections. The proposed motion model takes into account the mutual influences between the maneuvers performed by vehicles at an intersection. It also incorporates information about the influence of the geometry and topology of the intersection on the behavior of a vehicle, and therefore can be applied to arbitrary intersection layouts. The approach was validated with field trials using passenger vehicles equipped with Vehicle-to-Vehicle wireless communication modems, and in simulation. The results demonstrate that the algorithm is able to detect dangerous situations early and complies with real-time constraints

Context-based Estimation of Driver Intent at Road Intersections

International audienceNavigating through a road intersection is a complex manoeuvre that requires understanding the spatio-temporal relationships that exist between vehicles. Situation understanding and prediction are therefore fundamental functions for any computer-controlled safety or navigation system applied to road intersections. To interpret the situation at an intersection it is necessary to infer the intended manoeuvre of the relevant vehicles. Conventional approaches to manoeuvre prediction rely mainly on vehicle kinematics and dynamics. The contention of this paper is that contextual information in the form of topological and geometrical characteristics of the intersection can provide useful cues to understand the behaviour of a vehicle. We describe a probabilistic framework that extracts information from a digital map and uses it along with vehicle state information to estimate a driver's intended manoeuvre. The proposed approach is applicable to different types of intersections and handles uncertainty on the input information. We evaluate the performance of our approach on several real life scenarios using data recorded from real traffic

A Framework for Proactive Assistance: Summary

International audienceAdvanced Driving Assistance Systems usually provide assistance to drivers only once a high risk situation has been detected. Indeed, it is difficult for an embedded system to understand driving situations, and to predict early enough that it is to become uncomfortable or dangerous. Most of ADAS work assume that interactions between road entities do not exist (or are limited), and that all drivers react in the same manner in similar conditions. We propose a framework that enables to fill these gaps. On one hand, an ontology which is a conceptual description of entities present in driving spaces is used to understand how all the perceived entities interact together with the subject vehicle, and govern its behavior. On the other hand, a dynamic Bayesian Network enables to estimate the driver situation awareness with regard to the perceived objects, based on the ontology inferences, map information, driver actuation and driving style

Evaluating Risk at Road Intersections by Detecting Conflicting Intentions

This work proposes a novel approach to risk assessment at road intersections. Unlike most approaches in the literature, it does not rely on trajectory prediction. Instead, dangerous situations are identified by comparing what drivers intend to do with what they are expected to do. What a driver intends to do is estimated from the motion of the vehicle, taking into account the layout of the intersection. What a driver is expected to do is derived from the current configuration of the vehicles and the traffic rules at the intersection. The proposed approach was validated in simulation and in field experiments using passenger vehicles and Vehicle-to-Vehicle communication. Different strategies are compared to actively avoid collisions if a dangerous situation is detected. The results show that the effectiveness of the strategies varies with the situation.Ces travaux proposent une nouvelle approche pour l'évaluation du risque aux intersections. Contrairement aux approches traditionnelles, celle-ci ne se base pas sur de la prédiction de trajectoire. A la place, les situations dangereuses sont identifiées en comparant ce que les conducteurs ont l'intention de faire avec ce qu'ils devraient faire. L'intention d'un conducteur est estimée à partir du mouvement de son véhicule et de l'agencement de l'intersection. Pour déterminer ce qu'un conducteur devrait faire, la configuration actuelle des véhicules dans la scène est prise en compte, ainsi que les règles de la circulation. L'approche proposée a été validée en simulation et au cours de tests réels avec des véhicules de série équipés de modems de communication V2V. Différentes stratégies sont comparées pour l'évitement de collision lorsqu'une situation dangereuse est détectée. Les résultats montrent que l'efficacité des stratégies varie avec la situation

Trajectory Prediction of Traffic Agents: Incorporating context into machine learning approaches

International audienceFor a vehicle to navigate autonomously, it needs to perceive its surroundings and estimate the future state of the relevant traffic-agents with which it might interact as it navigates across public road networks. Predicting the future state of the perceived entities is a challenge, as these might appear to move in a stochastic manner. However, their motion is constrained to an extent by context, in particular the road network structure. Conventional machine learning methods are mainly trained using data from the perceived entities without considering roads, as a result trajectory prediction is difficult. In this paper, the notion of maps representing the road structure are included into the machine learning process. For this purpose, 3D LiDAR points and maps in the form of binary masks are used. These are used on a recurrent artificial neural network, the LSTM encoder-decoder based architecture to predict the motion of the interacting traffic agents. A comparison between the proposed solution with one that is only sensor driven (LiDAR) is included. For this purpose, NuScenes dataset is utilised, that includes annotated 3D point clouds. The results have demonstrated the importance of context to enhance our prediction performance as well as the capability of our machine learning framework to incorporate map information

Modelling Dynamic Scenes at Unsignalised Road Intersections

Understanding dynamic scenes at road intersections is both crucial and challenging for intelligent vehicles. In order to detect potentially dangerous situations, algorithms are needed that can interpret the behaviour of the actors in the scene and predict its likely evolution. The difficulty of this task arises from the large number of possible scenarios. The conventional answer to this issue is to discard vehicle interactions in the manoeuvre prediction process, i.e. to infer the manoeuvre performed by each vehicle from its past and current behaviour, independently from the other vehicles in the scene. In this paper we show how this affects collision risk estimation in very common scenarios, making it unusable in practice for Advanced Driver Assistance Systems (ADAS) applications. As an alternative we propose a probabilistic model for vehicles traversing unsignalised intersections that accounts for the mutual influence between vehicle manoeuvres. The focus is on the utilisation of contextual information (i.e. layout of the intersection, presence of other vehicles and traffic rules) to interpret a vehicle's behaviour. We show how the model can be used for accurate situation and risk assessment

Probabilistic Decision-Making at Road Intersections: Formulation and Quantitative Evaluation

International audienceAs drivers approach a road intersection, they must decide whether to cross it or to come to a stop. For this purpose, drivers make a situation assessment and adapt their behaviour accordingly. When this task is performed by a computer, the available information is partial and uncertain. Any decision requires the system to use this information as well as taking into account the behaviour of other drivers to avoid collisions. However, metrics such as collision rate can remain low in an interactive environment because of other driver’s actions. Consequently, evaluation metrics must depend on other driving aspects. In this paper a decision-making mechanism and metrics to evaluate such a system at road intersection crossing are presented. For the former, a Partially Observable Markov Decision Process is used to model the system with respect to uncertainties in the behaviour of other drivers. For the latter, different key performance indicators are defined to evaluate the resulting behaviour of the system with different configurations and scenarios. The approach is demonstrated within an automotive grade simulator. It has showed at times, that whilst the vehicle can cross safely the intersection, it might not satisfy other key performance indicators related to highway code

Noninvasive monitoring of serial changes in pulmonary vascular resistance and acute vasodilator testing using cardiac magnetic resonance

Objectives The study sought to evaluate the ability of cardiac magnetic resonance (CMR) to monitor acute and long-term changes in pulmonary vascular resistance (PVR) noninvasively. Background PVR monitoring during the follow-up of patients with pulmonary hypertension (PH) and the response to vasodilator testing require invasive right heart catheterization. Methods An experimental study in pigs was designed to evaluate the ability of CMR to monitor: 1) an acute increase in PVR generated by acute pulmonary embolization (n = 10); 2) serial changes in PVR in chronic PH (n = 22); and 3) changes in PVR during vasodilator testing in chronic PH (n = 10). CMR studies were performed with simultaneous hemodynamic assessment using a CMR-compatible Swan-Ganz catheter. Average flow velocity in the main pulmonary artery (PA) was quantified with phase contrast imaging. Pearson correlation and mixed model analysis were used to correlate changes in PVR with changes in CMR-quantified PA velocity. Additionally, PVR was estimated from CMR data (PA velocity and right ventricular ejection fraction) using a formula previously validated. Results Changes in PA velocity strongly and inversely correlated with acute increases in PVR induced by pulmonary embolization (r = –0.92), serial PVR fluctuations in chronic PH (r = –0.89), and acute reductions during vasodilator testing (r = –0.89, p ≤ 0.01 for all). CMR-estimated PVR showed adequate agreement with invasive PVR (mean bias –1.1 Wood units,; 95% confidence interval: –5.9 to 3.7) and changes in both indices correlated strongly (r = 0.86, p < 0.01). Conclusions CMR allows for noninvasive monitoring of acute and chronic changes in PVR in PH. This capability may be valuable in the evaluation and follow-up of patients with PH

Cooperative Vehicles to Facilitate the Crossing of Intersections by Emergency Service Vehicles

International audienceThe deployment of Wireless communications, localisation and perception technologies are enabling the sharing of information between passenger vehicles and with the infrastructure. An electronic horizon could be built that will be several times larger than what vehicle onboard perception systems could provide and likely at a lower cost. As a result the situational awareness of drivers of such vehicles will be enlarged allowing driving assistance systems to be more predictive. The results of a Use Case related to intersection safety, the crossing of an intersection by an Emergency Service Vehicle (ESV) is examined in details in this paper. The purpose is to convey the preliminary results of the technologies deployed as part of SAFESPOT, a large European project addressing safety related applications. It includes details of the various system components and analysis of the technologies deployed

Cooperative Vehicles to Facilitate the Crossing of Intersections by Emergency Service Vehicles

International audienceThe deployment of Wireless communications, localisation and perception technologies are enabling the sharing of information between passenger vehicles and with the infrastructure. An electronic horizon could be built that will be several times larger than what vehicle onboard perception systems could provide and likely at a lower cost. As a result the situational awareness of drivers of such vehicles will be enlarged allowing driving assistance systems to be more predictive. The results of a Use Case related to intersection safety, the crossing of an intersection by an Emergency Service Vehicle (ESV) is examined in details in this paper. The purpose is to convey the preliminary results of the technologies deployed as part of SAFESPOT, a large European project addressing safety related applications. It includes details of the various system components and analysis of the technologies deployed