The article deals with the analysis and interpretation 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, telemetric and inertial data for environment monitoring. The sensor fusion uses our Bayesian Occupancy Filter for a spatio-temporal grid representation of the traffic scene. The underlying probabilistic approach is capable of dealing with uncertainties when modeling the environment as well as detecting and tracking dynamic objects. The collision risks are estimated as stochastic variables and are predicted for a short period ahead with the use of Hidden Markov Models and Gaussian processes. The software implementation takes advantage of our methods, which allow for parallel computation. Our tests have proven the relevance and feasibility of our approach for improving the safety of car driving.

/pdf/probabilistic-analysis-of-dynamic-scenes-and-collision-risks-wrwulfq5pt.pdf

Probabilistic Analysis of Dynamic Scenes and Collision Risks Assessment to Improve Driving Safety

This paper presents the generic concept of using cloud-based intelligent car parking services in smart cities as an important application of the Internet of Things (IoT) paradigm. This type of services will become an integral part of a generic IoT operational platform for smart cities due to its pure business-oriented features. A high-level view of the proposed middleware is outlined and the corresponding operational platform is illustrated. To demonstrate the provision of car parking services, based on the proposed middleware, a cloud-based intelligent car parking system for use within a university campus is described along with details of its design, implementation, and operation. A number of software solutions, including Kafka/Storm/Hbase clusters, OSGi web applications with distributed NoSQL, a rule engine, and mobile applications, are proposed to provide 'best' car parking service experience to mobile users, following the Always Best Connected and best Served (ABC&S) paradigm.

/pdf/a-cloud-based-car-parking-middleware-for-iot-based-smart-unrop79x3m.pdf

A Cloud-Based Car Parking Middleware for IoT-Based Smart Cities: Design and Implementation

Aspects of the invention relate generally to autonomous vehicles. The features described improve the safety, use, driver experience, and performance of these vehicles by performing a behavior analysis on mobile objects in the vicinity of an autonomous vehicle. Specifically, the autonomous vehicle is capable of detecting nearby objects, such as vehicles and pedestrians, and is able to determine how the detected vehicles and pedestrians perceive their surroundings. The autonomous vehicle may then use this information to safely maneuver around all nearby objects.

System and method for predicting behaviors of detected objects through environment representation

A vehicle maneuver application (VMA) interface, and a VMA interface system software being executed thereon, may be used for installing, modifying, uninstalling, activating, and/or deactivating one or more VMAs. The VMA interface may include an interface processor and an interface memory. The interface processor may be configured to install the VMA to the VMA device upon receiving an installation signal, update the installed VMA upon receiving an update signal, uninstalling the installed VMA upon receiving an uninstallation signal, activating the installed VMA upon receiving an activation signal, and/or deactivating the installed VMA upon receiving a deactivation signal. The interface memory may be coupled to the interface processor, and it may be configured to store a VMA execution record related to the installed VMA.

Vehicle maneuver application interface

In large parking areas such as those at mega shopping malls or stadiums, drivers always have difficulty to find vacant car- park lots especially during peak periods or when the parking lots are almost full. A solution to reduce the drivers' searching time for vacant car-park lots will greatly save time, reduce cost and improve the traffic flow in the car park areas. In this paper, a research project which was developed to acquire car-park occupancy information using integrated approach of image processing algorithms is presented. Motivation for developing this system came from the fact that minimum cost is involved because image processing technique is used rather than sensor-based techniques. Security surveillance cameras which are readily available in most car parks can be used to acquire the images of the car park. This solution is much cost effective than installing sensor on each parking lot. This project is called as Car-Park Occupancy Information System (COINS), and it was tested using simulation model and also in real-case scenarios.

/pdf/integrated-approach-in-the-design-of-car-park-occupancy-3t3qe4xjeo.pdf

Integrated Approach in the Design of Car Park Occupancy Information System (COINS)

The invention relates to a vehicle or traffic control method and to a vehicle or traffic control system. The vehicle or traffic control method comprises the steps: a) estimating actual and/or future behavior of a first traffic participant and of a second traffic participant, respectively, the second traffic participant being different from the first traffic participant, b) estimating a trajectory to be taken by the first traffic participant and/or a trajectory to be taken by the second traffic participant, c) determining risk of collision of the first traffic participant relative to the second traffic participant by calculating information adapted for risk assessment of collision of the first traffic participant relative to the second traffic participant, and d) controlling the behavior of the first traffic participant based on the information provided after step a), step b) and/or step c). In this way a probability value is determined which indicates the plausibility that a vehicle or traffic participant might enter into collision within a certain time horizon in the future.

Vehicle or traffic control method and system

In this project, we took on the task of localizing an automatic vehicle and building a map of the car park in real time. This takes place within the car park of INRIA Rhone-Alpes on the CyCab vehicle with a Sick laser range scanner. Our method uses only laser scanners to retrieve the position and orientations of vehicles in the car park. With the detected vehicles as landmarks, CyCab performs a localization of itself and builds a map of the car park at the same time. Classical clustering and segmentation techniques to extract line segments from the laser scan data are applied. The key contribution of the paper is the extraction of vehicle poses from the line segments using Bayesian programming. The method of FastSLAM is used in localizing CyCab and estimating the pose of vehicles in the car park. A set of hypotheses is obtained as a result. The second contribution is a method of combining the set of hypotheses together to form a final map of the car park.

/pdf/vehicle-detection-and-car-park-mapping-using-laser-scanner-35ggnl8lh6.pdf

Vehicle detection and car park mapping using laser scanner

In this paper, a method of obtaining vehicle hypothesis based on laser scan data only is proposed This is implemented on the robotic vehicle, CyCab, for navigation and mapping of the static car park environment Laser scanner data is used to obtain hypothesis on position and orientation of vehicles with Bayesian Programming Using the hypothesized vehicle poses as landmarks, CyCab performs Simultaneous Localization And Mapping (SLAM) A final map consisting of the vehicle positions in the car park is obtained

/pdf/online-reconstruction-of-vehicles-in-a-car-park-1basn05xjx.pdf

Online Reconstruction Of Vehicles In A Car Park

The Inria CHROMA research team develops new algorithms and methods for robotics, taking into
consideration autonomy, limited resources, cooperation and social interactions. One of its main
application domains are perception and decision making for driver assistance systems or autonomous
cars. This work is done in the scope of long term collaborations with major automotive manufacturers
such as Toyota and Renault. The approach for the perception of dynamic environment is based on
sensor fusion and temporal filtering in occupancy grids using a probabilistic framework. The method is
based on the merging of various sensor data into a probabilistic grid resulting in a joint estimation of
the spatial occupancy and dynamics. It has been implemented using the Hybrid Sampling Bayesian
Occupancy Filter paradigm (HSBOF) [1], and later extended with the Conditional Monte Carlo Dense
Occupancy Tracker (CMCDOT) [2] to explicitly represent areas observability and efficiently extracts
objects using a light-cost algorithm based on adaptive sampling of dynamic parts. This approach leads
to greatly improve the quality of the results and to drastically decrease the computation and memory
costs. The CMCDOT has been implemented and highly optimized using CUDA and embedded on
several experimental platforms (Titan X, Jetson TK1, TX1) for performing real-time scene analysis.
The outputs of this algorithm is used to perform risk estimation [3] and could be used in many
intelligent vehicle applications such as emergency braking, obstacle avoidance or automatic driving.
V2X communications between cars and infrastructures are also used to communicate objects position
and to improve collision detection in blind spots or areas with low visibility. In this context, light
connected perception units have been developed to be easily placed on dangerous road areas for
warning connected cars of upcoming collisions.
To perform experiments a Lexus LS 600h vehicle has been equipped with several sensors (2D Lidars,
cameras, GPS, IMU) in cooperation with Toyota, as well as a Renault Zoe vehicle (3D and 2D Lidars,
cameras, GPS, IMU) and the specifically designed Perception units (2D Lidars, cameras, Jetson TX1)
in the scope of the French `Technological Research Institute Nanoelec'.
References:
[1] Amaury Negre, Lukas Rummelhard, and Christian Laugier. Hybrid Sampling Bayesian Occupancy
Filter. In IEEE Intelligent Vehicles Symposium (IV), Dearborn, United States, Jun. 2014.
[2] Lukas Rummelhard, Amaury Negre, and Christian Laugier. Conditional Monte Carlo Dense
Occupancy Tracker. In 18th IEEE International Conference on Intelligent Transportation Systems, Las
Palmas, Spain, Sep. 2015.
[3] Lukas Rummelhard, Amaury Negre, Mathias Perrollaz, and Christian Laugier. Probabilistic Gridbased
Collision Risk Prediction for Driving Application. In ISER, Marrakech/Essaouira, Morocco, Jun.
2014

https://hal.inria.fr/hal-01428547/document

Christopher Tay Meng Keat

Papers

Vehicle or traffic control method and system

Vehicle detection and car park mapping using laser scanner

Online Reconstruction Of Vehicles In A Car Park

Intelligent Perception and Situation Awareness for Automated vehicles ADAS & Autonomous Driving