Javier Ibanez-Guzman

Risk assessment at road intersections: Comparing intention and expectation

Intersections are the most complex and hazardous areas of the road network, and 89% of accidents at intersection are caused by driver error. We focus on these accidents and propose a novel approach to risk assessment: in this work dangerous situations are identified by detecting conflicts between intention and expectation, i.e. between what drivers intend to do and what is expected of them. Our approach is formulated as a Bayesian inference problem where intention and expectation are estimated jointly for the vehicles converging to the same intersection. This work exploits the sharing of information between vehicles using V2V wireless communication links. The proposed solution was validated by field experiments using passenger vehicles. Results show the importance of taking into account interactions between vehicles when modeling intersection situations.

Vehicle following with obstacle avoidance capabilities in natural environments

A robust vehicle following system with obstacle avoidance capabilities for operation in natural environments is described in this paper. By combining a novel vehicle-tracking and detection algorithm with our path-planner for autonomous navigation, it was possible for a tracked logistics armoured ambulance carrier to follow a multi purpose vehicle in an equatorial jungle where few non-paved roads and markers exist. With this new approach, vehicle following performance is enhanced and vehicle safety ensured. Field trials performed in tropical jungle conditions have demonstrated the validity of the approach; results from the field works are included and discussed in this paper.

Vehicle to vehicle communications applied to road intersection safety, field results

Road intersections represent one of the most complex configurations encountered when traversing road networks. A high percentage of accidents occur at these locations. The introduction of wireless communications technologies onboard passenger vehicles is enabling the sharing of information and through it enhancing the situational awareness of vehicle drivers. In this paper the implementation of safety applications for cooperative vehicle systems is presented applied to road intersection safety. The system relies on three fundamental technologies: communications, localization and the modelling of the environment surrounding the subject vehicle. The paper centres in a case study, the priority crossing of an Emergency Service Vehicle at an intersection. An analysis of the issues identified during the implementation and testing is included. The implementation represents an instance of the architecture developed for cooperative vehicles applications as part of the European project, SAFESPOT.

Evaluating risk at road intersections by detecting conflicting intentions

This paper 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. Driver intentions and expectations are estimated from the joint motion of the vehicles, taking into account the layout of the intersection and the traffic rules at the intersection. The proposed approach was evaluated in simulation with two vehicles involved in typical collision scenarios. An analysis of the collision prediction horizon allows to characterize the efficiency of the approach in different situations, as well as the potential of different strategies to avoid an accident after a dangerous situation is detected.

Ontology-based context awareness for driving assistance systems

Within a vehicle driving space, different entities such as vehicles and vulnerable road users are in constant interaction which governs their behaviour. Whilst smart sensors provide information about the state of the perceived objects, considering the spatio-temporal relationships between them with respect to the subject vehicle remains a challenge. This paper proposes to fill this gap by using contextual information to infer how perceived entities are expected to behave, and thus what are the consequences of these behaviours on the subject vehicle. For this purpose, an ontology is formulated about the vehicle, perceived entities and context (map information) to provide a conceptual description of all road entities with their interaction. It allows for inferences of knowledge about the situation of the subject vehicle with respect to the environment in which it is navigating. The framework is applied to the navigation of a vehicle as it approaches road intersections, to demonstrate its applicability. Results from the real-time implementation on a vehicle operating under controlled conditions are included. They show that the proposed ontology allows for a coherent understanding of the interactions between the perceived entities and contextual data. Further, it can be used to improve the situation awareness of an ADAS (Advanced Driving Assistance System), by determining which entities are the most relevant for the subject vehicle navigation.

Lane marking aided vehicle localization

A localization system that exploits L1-GPS estimates, vehicle data, and features from a video camera as well as lane markings embedded in digital navigation maps is presented. A sensitivity analysis of the detected lane markings is proposed in order to quantify both the lateral and longitudinal errors caused by 2D-world hypothesis violation. From this, a camera observation model for vehicle localization is proposed. The paper presents also a method to build a map of the lane markings in a first stage. The solver is based on dynamical Kalman filtering with a two-stage map-matching process which is described in details. This is a software-based solution using existing automotive components. Experimental results in urban conditions demonstrate an significant increase in the positioning quality.

Context-based estimation of driver intent at road intersections

Navigating 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 drivers 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.

Modelling stop intersection approaches using Gaussian processes

Each driver reacts differently to the same traffic conditions, however, most Advanced Driving Assistant Systems (ADAS) assume that all drivers are the same. This paper proposes a method to learn and to model the velocity profile that the driver follows as the vehicle decelerates towards a stop intersection. Gaussian Processes (GP), a machine learning method for non-linear regressions are used to model the velocity profiles. It is shown that GP are well adapted for such an application, using data recorded in real traffic conditions. GP allow the generation of a normally distributed speed, given a position on the road. By comparison with generic velocity profiles, benefits of using individual driver patterns for ADAS issues are presented.

A collaborative-shared control system with safe obstacle avoidance capability

Tele-operated systems allow humans to extend their physical capabilities and enable them to intervene in hazardous operations or where their presence is not possible. However, the operation of such systems over long periods has proved to be difficult and stressful. Consequently, means to facilitate their use are the subject of much study and experimental work. In this paper, we propose a collaborative-shared control strategy that combines the operator abilities with robotic-based tasks to render these systems more flexible and robust. In our method, the collaborative control component is responsible for allowing operator intervention when the robot is facing complex situations, whilst the shared control component provides an automatic control mechanism to assist and to monitor-correct irrational operator actions. The paper demonstrates how collaborative and shared control strategies work together to facilitate the teleoperated control of a mobile platform in a cluttered environment. The experimental results include applications to surveillance and to search & rescue operations. In addition, a key component in the form of a hybrid obstacle avoidance module is introduced that allows the robot to be guided on a task basis by the operator at a distance.

Target-tracking and path planning for vehicle following in jungle environment

In this paper, we proposed a robot vehicle following algorithm which can navigate a 10 ton armored personnel carrier to follow a leading vehicle (MPV) in the jungle. This algorithm comprises two components, the first one is a target tracking module which can detect and track the lead vehicle from the measurements of a SICK laser scanner; the second one is a obstacle avoidance module which takes the tracking results and the local environment description as input, it then generates the set-points for the vehicle to follow. Plenty of trials have been carried out in the jungle of Singapore, our techniques validity and robust is demonstrated and tested, the results will be showed in this paper.