Christian Laugier

• PhD and State Doctor in CS - Research Director
• Managing Director at National Institute for Research in Computer Science and Control

Object detection is a critical problem for the safe interaction between autonomous vehicles and road users. Deep-learning methodologies allowed the development of object detection approaches with better performance. However, there is still the challenge to obtain more characteristics from the objects detected in real-time. The main reason is that m...

Perception systems on autonomous vehicles have the challenge of understanding the traffic scene in different situations. The fusion of redundant information obtained from different sources has been shown considerable progress under different methodologies to achieve this objective. However, new opportunities are available to obtain better fusion re...

Traditionally, point cloud-based 3D object detectors are trained on annotated, non-sequential samples taken from driving sequences (e.g. the KITTI dataset). However, by doing this, the developed algorithms renounce to exploit any dynamic information from the driving sequences. It is reasonable to think that this information, which is available at t...

Detection of the objects around a vehicle is important for a safe and successful navigation of an autonomous vehicle. Instance segmentation provides a fine and accurate classification of the objects such as cars, trucks, pedestrians, etc. In this study, we propose a fast and accurate approach which can detect and segment the object instances which...

Ground plane estimation and ground point seg-mentation is a crucial precursor for many applications in robotics and intelligent vehicles like navigable space detection and occupancy grid generation, 3D object detection, point cloud matching for localization and registration for mapping. In this paper, we present GndNet, a novel end-to-end approach...

Semantic information provides a valuable source for scene understanding around autonomous vehicles in order to plan their actions and make decisions; however, varying weather conditions reduce the accuracy of the semantic segmentation. We propose a method to adapt to varying weather conditions without supervision, namely without labeled data. We up...

Through the media almost on a daily basis, we hear of the benefits and potential brought by Autonomous Vehicles to society. This is referred in terms of accessibility to land transportation for people unable to drive, improving driver productivity by reducing or eliminating altogether the driving load and improving safety by minimising driver error...

Accurate detection of objects in 3D point clouds is a central problem for autonomous navigation. Most existing methods use techniques of hand-crafted features representation or multi-sensor approaches prone to sensor failure. Approaches like PointNet that directly operate on sparse point data have shown good accuracy in the classification of single...

We propose semantic grid, a spatial 2D map of the environment around an autonomous vehicle consisting of cells which represent the semantic information of the corresponding region such as car, road, vegetation, bikes, etc. It consists of an integration of an occupancy grid, which computes the grid states with a Bayesian filter approach, and semanti...

In an autonomous vehicle setting, we propose a method for the estimation of a semantic grid, i.e. a bird's eye grid centered on the car's position and aligned with its driving direction, which contains high-level semantic information about the environment and its actors. Each grid cell contains a semantic label with divers classes, as for instance...

When approaching a road intersection, drivers consider several factors and choose amongst different likely manoeuvres. For an autonomous agent, it is fundamental to understand what other drivers are doing before deciding their own manoeuvres. These are seldom be the same as intersections differ and the situations too. Whilst, learning techniques ca...

We address the problem of multi-vehicle tracking and motion prediction in highway scenarios using information from sensors and perception systems widely used in automated driving. In particular, we focus on the detection of lane change maneuvers. Dangerous lane changing constitutes the main cause of highway accidents and a reliable detection system...

In the recent years, more and more modern cars have been equipped with perception capabilities. One of the key applications of such perception systems is the estimation of a risk of collision. This is necessary for both Advanced Driver Assistance Systems and Autonomous Navigation. Most approach for risk estimation propose to detect and track the dy...

One of the key factors to ensure the safe operation of autonomous and semi-autonomous vehicles in dynamic environments is the ability to accurately predict the motion of the dynamic obstacles in the scene. In this work, we show how to use a realistic driver model learned from demonstrations via Inverse Reinforcement Learning to predict the long-ter...

Occupancy Grids (OGs) are a popular framework for robotic perception. They were recently adopted for performing multi-sensor fusion and environment mapping for autonomous vehicles. However, high computational requirements strongly hinder their integration into less powerful automotive ECUs. To overcome this problem, we propose an algorithmic improv...

This chapter describes the emerging robotics application field of intelligent vehicles – motor vehicles that have autonomous functions and capabilities. The chapter is organized as follows. Section 62.1 provides a motivation for why the development of intelligent vehicles is important, a brief history of the field, and the potential benefits of the...

Presents a listing of the senior associates editors for the IEEE Transactions on Intelligent Vehicles.

Proper modeling of dynamic environments is acore task in the field of intelligent vehicles. The most commonapproaches involve the modeling of moving objects, throughDetection And Tracking of Moving Objects (DATMO) methods.An alternative to a classic object model framework is theoccupancy grid filtering domain. Instead of segmenting the sceneinto ob...

This paper 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 p...

The four articles in this special section focus on planning and development of autonomous vehicles. These contributions discuss important aspects that mark trends in autonomous driving as well as complete system architectures of successful implementations. Foremost, maps have enabled autonomous driving over long distances and it comes hardly as a s...

Intelligent vehicles (IVs) need a perception system to model the surrounding environment. The Hybrid Sampling Bayesian Occupancy Filter (HSBOF) is a perception algorithm monitoring a grid-based model of the environment called "occupancy grid". It is a highly data-parallel algorithm and requires a high computational performance to be executed in rea...

This work proposes a different form of robotic navigation in dynamic environments, where the robot takes advantage of the motion of pedestrians, in order to improve its own navigation capabilities. Instead of treating persons as dynamic obstacles that should be avoided, here they are treated as special agents with an expert knowledge on navigating...

Research on autonomous car driving and advanced driving assistance systems has come to occupy a very significant place in robotics research. On the other hand, there are significant entry barriers (eg cost, legislation, logistics) that make it very difficult for small research groups and individual researchers to have access to a real autonomous ve...

Approaching a group of humans is an important navigation task. Although many methods have been proposed to avoid interrupting groups of people engaged in a conversation, just a few works have considered the proper way of joining those groups. Research in the field of social sciences have proposed geometric models to compute the best points to join...

Service and assistance robots that must move in human environment must address the difficult issue of navigating in dynamic environments. As it has been shown in previous works, in such situations the robots can take advantage of the motion of persons by following them, managing to move together with humans in difficult situations. In those circums...

With the objective to improve road safety, the automotive industry is moving toward more “intelligent” vehicles. One of the major challenges is to detect dangerous situations and react accordingly in order to avoid or mitigate accidents. This requires predicting the likely evolution of the current traffic situation, and assessing how dangerous that...

In the recent years, more and more modern cars have been equipped with perception capabilities. One of the key applications of such perception systems is the estimation of a risk of collision. This is nec-essary for both Advanced Driver Assistance Systems and Autonomous Navigation. Most approach for risk estimation propose to detect and track the d...

Modeling and monitoring dynamic environments is a complex task but is crucial in the field of intelligent vehicle. A traditional way of addressing these issues is the modeling of moving objects, through Detection And Tracking of Moving Objects (DATMO) methods. An alternative to a classic object model framework is the occupancy grid filtering domain...

Predicting the future speed of the ego-vehicle is a necessary component of many Intelligent Transportation Systems (ITS) applications, in particular for safety and energy management systems. In the last four decades many parametric speed prediction models have been proposed, the most advanced ones being developed for use in traffic simulators. More...

With decreasing costs in robotic platforms, mobile robots that provide assistance to humans are becoming a reality. A key requirement for these types of robots is the ability to efficiently and safely navigate in populated environments. This work proposes to address this issue by studying how robots can select and follow human leaders, to take adva...

Intensity and Depth Data Integration for Vehicle Detection

This paper presents a novel method to perform the outlier rejection task between two different views of a camera rigidly attached to an Inertial Measurement Unit (IMU). Only two feature correspondences and gyroscopic data from IMU measurerments are used to compute the motion hypothesis. By exploiting this 2-point motion parametrization, we propose...

In the context of a growing interest in modelling human behavior to increase the robots’ social abilities, this article presents a survey related to socially-aware robot navigation. It presents a review from sociological concepts to social robotics and human-aware navigation. Social cues, signals and proxemics are discussed. Socially aware behavior...

The Bayesian occupancy filter (BOF) provides a framework for grid-based monitoring of the dynamic environment. It allows us to estimate dynamic grids, containing both information of occupancy and velocity. Clustering such grids then provides detection of the objects in the observed scene.

User privacy is a requirement for wireless vehicular communications, and a number of privacy protection strategies have already been developed and standardized. In particular, methods relying on the use of temporary pseudonyms and silent periods have proved their ability to confuse attackers who would attempt to track vehicles. In addition to their...

In this paper, an object class recognition method is presented. The method uses local image features and follows the part-based detection approach. It fuses intensity and depth information in a probabilistic framework. The depth of each local feature is used to weigh the probability of finding the object at a given distance. To train the system for...

In this paper, a method to perform semi-autonomous navigation on a wheelchair is presented. The wheelchair could be controlled in semi-autonomous mode estimating the user's intention by using a face pose recognition system or in manual mode. The estimator was performed within a Bayesian network approach. To switch these two modes, a speech interfac...

For collision avoidance systems to be accepted by human drivers, it is important to keep the rate of unnecessary interventions very low. This is challenging since the decision to intervene or not is based on incomplete and uncertain information. The contribution of this paper is a decision making strategy for collision avoidance systems which allow...

With robots technology shifting towards entering human populated environments, the need for augmented perceptual and planning robotic skills emerges that complement to human presence. In this integration, perception and adaptation to the implicit human social conventions plays a fundamental role. Toward this goal, we propose a novel framework that...

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 pr...

We propose a novel method to estimate the relative motion between two consecutive camera views, which only requires the observation of a single feature in the scene and the knowledge of the angular rates from an inertial measurement unit, under the assumption that the local camera motion lies in a plane perpendicular to the gravity vector. Using th...

Service robots have a great potential of improving human quality of life by aiding in everyday tasks. However, robots that share an environment and interact with humans still face some challenges that limits their acceptance. One of these challenges is how to move and behave among groups of people, which is a task performed seamlessly by humans and...

Predicting driver behavior is a key component for Advanced Driver Assistance Systems (ADAS). In this paper, a novel approach based on Support Vector Machine and Bayesian filtering is proposed for online lane change intention prediction. The approach uses the multiclass probabilistic outputs of the Support Vector Machine as an input to the Bayesian fi...

In this paper we present some important improvements to a fast motion detection technique based on laser data and odometry/imu information. This technique instead of performing a complete SLAM (Simultaneous Localization and Mapping) solution, is based on transferring occupancy information between two consecutive data grids. Then we show its integra...

A crucial requirement for service robots is to be able to move in dynamic environments shared with humans as well as interact with them. Navigation in such environments is a challenging task, as the environment is constantly changing, future states have to be predicted and planning and execution must be carried on-line. However, even in very comple...

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 mot...

A crucial requirement for service robots is to be able to move in dynamic environments shared with humans as well as interact with them. Navigation in such environments is a challenging task, as the environment is constantly changing, future states have to be predicted and planning and execution must be carried on-line. However, even in very comple...

In this article a method to perform semi- autonomous navigation on a wheelchair is presented, contextual information from the environment as user's habits and points of interest are employed to infer the user's desired destination in a global map. Illogical steering signals coming from the user- machine interface input are filtered out to improve t...

Ensuring proper living conditions for an ever growing number of elderly people is an important challenge for many countries. The difficulty and cost of hiring and training specialized personnel has fostered research in assistive robotics as a viable alternative. In particular, this paper studies the case of a robotic wheelchair, specifically its au...

Occupancy grids are a very convenient tool for environment representation in robotics. This paper will detail a novel approach for computing occupancy grids from stereo vision and show its application to intelligent vehicles. In the proposed approach, occupancy is initially computed directly in the stereoscopic sensor's disparity space. The calcula...

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...

Ensuring proper living conditions for an ever growing number of elderly people is a significative challenge for many countries. The difficulty and cost of hiring and training specialized personnel has fostered research in assistive robotics as a viable alternative. In this context, an ideally suited and very relevant application is to transport peo...

Perception is a key component for any robotic system. In this paper we present a method to construct occupancy grids by fusing sensory information using Linear Opinion Pools. We used lidar sensors and a stereo-vision system mounted on a vehicle to make the experiments. To perform the validation, we compared the proposed method with the fusion metho...

The objective of this paper is to present a strategy to safely move a robot in an unknown and complex environment where people are moving and interacting. The robot, by using only its sensor data, must navigate respecting humans' comfort. To obtain good results in such a dynamic environment, a prediction on humans' movement is also crucial. To solv...

Validation of algorithms developed by assistance robotics research on real platforms is essential. Producing robotic architectures that promote scientific advances while regarding usability for the final user is a challenging issue where an appropriate trade-off between both requirements must be found. This paper proposes a new framework for the de...

The recent development of a new kind of public transportation system relies on a particular double-steering kinematic structure enhancing maneuverability in cluttered environments such as downtown areas. We call bi-steerable car a vehicle showing this kind of kinematics. Endowed with autonomy capacities, the bi-steerable car ought to combine suitab...

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 vehi...

Vehicles are evolving into autonomous mobile-connected platforms. The rationale resides on the political and economic will towards a sustainable environment as well as advances in information and communication technologies that are rapidly being introduced into modern passenger vehicles. From a user perspective, safety and convenience are always a...

The development of autonomous vehicles garnered an increasing amount of attention in recent years. The interest for automotive industries is to produce safer and more user-friendly cars. A common reason behind most traffic accidents is the failure on the part of the driver to adequately monitor the vehicle’s surroundings. This chapter addresses the...

This chapter addresses autonomous navigation in populated and dynamic environments. Unlike static or controlled environments where global path planning approaches are suitable, dealing with highly dynamic and uncertain environments requires to address simultaneously many difficult issues: the detection and tracking of the moving obstacles, the pred...

This chapter describes detection and tracking of moving objects (DATMO) for purposes of autonomous driving. DATMO provides awareness of road scene participants, which is important in order to make safe driving decisions and abide by the rules of the road. Three main classes of DATMO approaches are identified and discussed. First is the traditional...

The article deals with the analysis and interpre-tation of dynamic scenes typical of urban driving. The key objective is to assess risks of collision for the ego-vehicle. We describe our concept and methods, which we have integrated and tested on our experimental platform on a Lexus car and a driving simulator. The on-board sensors deliver visual,...

With the growing demand of personal assistance to mobility and mobile service robotics, robot navigation systems must be “aware” of the social conventions followed by people. They must respect proximity constraints but also respect people interacting. For example, they may not break interaction between people talking, unless the occupants want to t...

In this work an object class recognition method is presented. The method uses local image features and follows the part based detection approach. It fuses intensity and depth information in a probabilistic framework. The depth of each local feature is used to weigh the probability of finding the object at a given scale. To train the system for an o...

Fusion of telemetric and visual data from traffic scenes helps exploit synergies between different on-board sensors, which monitor the environment around the ego-vehicle. This paper outlines our approach to sensor data fusion, detection and tracking of objects in a dynamic environment. The approach uses a Bayesian Occupancy Filter to obtain a spati...

Safety applications at road intersections require algorithms that can estimate the manoeuvre intention of all the drivers in the scene. In this paper, the use of contextual information extracted from a digital map of the road network is explored. We propose a Bayesian network which combines probabilistically uncertain observations on the vehicle's...

Navigating through a road intersection is a complex manoeuvre that requires understanding the spatio-temporal re- lationships 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...