Intelligent vehicles and road transportation systems (ITS) Week 9 : - PowerPoint PPT Presentation

ME470 Intelligent vehicles and road transportation systems (ITS) Week 9 : Multi-vehicle cooperative and collaborative scenarios Denis Gingras January 2015 1 1-févr.-15 D Gingras – ME470 IV course CalPoly Week 9

Course outline  Week 1 : Introduction to intelligent vehicles, context, applications and motivations  Week 2 : Vehicle dynamics and vehicle modelling  Week 3: Positioning and navigation systems and sensors  Week 4: Vehicular perception and map building  Week 5 : Multi-sensor data fusion techniques  Week 6 : Object detection, recognition and tracking  Week 7: ADAS systems and vehicular control  Week 8 : VANETS and connected vehicles  Week 9 : Multi-vehicular cooperative and collaborative scenarios  Week 10 : The future: toward autonomous vehicles and automated driving (Final exam) D Gingras – ME470 IV course CalPoly Week 9 2 1-févr.-15

Week 9 outline  Brainstorming and introduction  Context and motivation for vehicular cooperation and collaboration  Cooperation for improved positioning and navigation  Cooperation for extended perception  Collaboration for traffic optimization  Collaboration for platooning  Centralized architectures  Decentralized architectures  How to select the proper information to exchange?  The over-convergence problem  The deadlock problem  Some safety examples in highway and urban scenarios  Cooperation and collaboration for driving automation D Gingras – ME470 IV course CalPoly Week 9 3 1-févr.-15

Brainstorming Brainstorming Open questions and introductory discussion Why considering multi vehicle architectures? D Gingras – ME470 IV course CalPoly Week 9 4 1-févr.-15 4

Brainstorming Brainstorming Open questions and introductory discussion What is collaboration? D Gingras – ME470 IV course CalPoly Week 9 5 1-févr.-15 5

Brainstorming Brainstorming Open questions and introductory discussion What is cooperation? D Gingras – ME470 IV course CalPoly Week 9 6 1-févr.-15 6

Brainstorming Brainstorming Open questions and introductory discussion What do we need to achieve cooperation and collaboration? D Gingras – ME470 IV course CalPoly Week 9 7 1-févr.-15 7

Brainstorming Brainstorming Open questions and introductory discussion For what kind of applications do we need cooperation? D Gingras – ME470 IV course CalPoly Week 9 8 1-févr.-15 8

Brainstorming Brainstorming Open questions and introductory discussion For what kind of applications do we need collaboration? D Gingras – ME470 IV course CalPoly Week 9 9 1-févr.-15 9

Brainstorming Brainstorming Open questions and introductory discussion What are the advantages of multi-vehicle approaches D Gingras – ME470 IV course CalPoly Week 9 10 1-févr.-15 10

Brainstorming Brainstorming Open questions and introductory discussion What are the drawbacks of multi-vehicle cooperative approaches D Gingras – ME470 IV course CalPoly Week 9 11 1-févr.-15 11

Brainstorming Brainstorming Open questions and introductory discussion What would be some possible criteria for priority assignment in vehicular data exchange ? D Gingras – ME470 IV course CalPoly Week 9 12 1-févr.-15 12

Brainstorming Brainstorming Open questions and introductory discussion What are the parameters that define a group of vehicles? D Gingras – ME470 IV course CalPoly Week 9 13 1-févr.-15 13

Brainstorming Brainstorming Open questions and introductory discussion What are the differences between a centralized and a decentralized approach in multi-vehicular scenarios? D Gingras – ME470 IV course CalPoly Week 9 14 1-févr.-15 14

Introduction Introduction Motivation for cooperation  Single vehicle solutions do not account for extra positional information available from surrounding vehicles  Cooperation among vehicles has the potential to: Improve positioning/localization accuracy & reliability of each  vehicles Enhance the range of perception (extended environment map )  Mitigate occlusion problems  Allow better prediction of communicating vehicles  Improve quality of navigation information and map matching  Mitigate variability in signal characteristics and environmental  conditions D Gingras – ME470 IV course CalPoly Week 9 15 1-févr.-15

Introduction Introduction Motivation  Cooperative vehicles have the potential to better perform tasks than isolated single vehicle because they  Share complementary information (improve accuracy)  Share redundant information (improve robustness/reliability)  In principle, more information about a phenomenon can be gathered from multiple measurements  Exploit multiple sensors within vehicles (GPS, inertial, etc.)  Cluster of vehicles seen as multiple sources of information  Limited local information gathered by a single vehicle requires collaboration to resolve inconsistencies between measurements, such as those due to malfunctioning sensors (ex. loss of GPS signal).  The attractiveness of cooperation in ad-hoc networks lies in its independence from any major additional infrastructure other than the vehicular communication systems. D Gingras – ME470 IV course CalPoly Week 9 16 1-févr.-15

Introduction Introduction Assumptions Vehicles are seldomly alone  Vehicles have sensing, computing and communicating  capabilities Vehicles are “made aware” of surrounding vehicles (relative  position) and local environment  Cooperative approaches combine information from multiple vehicles to construct a larger, more accurate environmental map of their surrounding, including their respective absolute and relative positions, beyond what is possible from a single vehicle. D Gingras – ME470 IV course CalPoly Week 9 17 1-févr.-15

Introduction Introduction Typical collaborative system main components. Note: collaboration often implies cooperation as well as indicated here. Source: Baber J et al., Collaborative Autonomous Driving, Intelligent Vehicles Sharing City Roads, IEEE Robotics & Automation Magazine, 2005 D Gingras – ME470 IV course CalPoly Week 9 18 1-févr.-15

Introduction Introduction Gain in safety with Cooperative ITS Effects of Vehicle-Infrastructure Cooperative Systems that support driving Source: Toyota support driving and aim to prevent traffic accidents by notifying drivers of information, which cannot be detected by a vehicle's own sensors, through communications between vehicles and infrastructure, or among vehicles. D Gingras – ME470 IV course CalPoly Week 9 19 1-févr.-15

Introduction Introduction Gain in safety with Cooperative ITS An intelligent Cooperative Control Architecture Source: Redding J. et al., An Intelligent Cooperative Control Architecture, D Gingras – ME470 IV course CalPoly Week 9 20 1-févr.-15

Introduction Introduction Tight coupling in cooperative vehicle safety (CVS)  In CVS systems, vehicles broadcast their physical state information over a shared wireless network to allow their neighbors to track them and predict possible collisions.  The physical dynamics of vehicle movement and the required accuracy from tracking process dictate certain load on the communication network.  The network performance is directly affected by the amount of offered load, and in turn directly affects the tracking process and its required load.  The tight mutual dependence of physical dynamics of vehicle (physical component), estimation/tracking process and communication process (cyber components) require a new look at how such systems are Source: Toyota designed and operated. D Gingras – ME470 IV course CalPoly Week 9 21 1-févr.-15

Introduction Introduction Design of cooperative multi-vehicular system  A typical cooperative architecture consist of the following tasks: Detect nearby vehicles in a given range;  Determine cluster topology and vehicle membership;  Estimate and register absolute position (local data fusion) of each vehicle  member in the same reference coordinate frame; Estimate inter-vehicular distances, heading (relative positions) of member  vehicles; Compute confidence interval on all local estimates: measures the  accuracy/uncertainty of the local absolute/relative position of estimates; Select relevant local estimates and uncertainty data for broadcasting to  vehicle members (data positioning integrity); Register remote data with local data in each vehicle (in time and space)  Include remote data to local data in fusion systems in order to perform  global fusion and improve local estimates (position or perception information) of individual vehicle members. D Gingras – ME470 IV course CalPoly Week 9 22 1-févr.-15

Introduction Introduction Multiple stack layered architecture for collaborative driving system Source: Lin S-p et al., A Multiple Stack Architecture for Intelligent Vehicles, IEEE Intelligent Vehicles Symposium (IV) June 8-11, 2014. Dearborn, Michigan, USA , 2014 D Gingras – ME470 IV course CalPoly Week 9 23 1-févr.-15

Introduction Introduction Emergency response vehicles with right-of-way, such as ambulances or fire trucks, could alert vehicles nearing an intersection that they were approaching, to more safely and efficiently pass through traffic. Source: SrwI Texas D Gingras – ME470 IV course CalPoly Week 9 24 1-févr.-15