Sensing for ADAS & Automated Driving a vision of present and future needs

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1 Sensing for ADAS & Automated Driving a vision of present and future needs Paul Widdowson Vision & Guidance Projects Leader Jaguar Land Rover Research

2 Presentation Contents Setting the Scene 1. What is ADAS? 2. ADAS into Autonomy 3. Journey to Intelligent Driving Features and Sensing 1. Features 2. Current Sensing 3. Importance of Image Processing 4. Other sensing & fusion Bringing It all together 1. Situational Awareness and Sensing 2. Conclusions 2

3 Setting the scene

4 What is ADAS? When we are talking about ADAS (Advanced Driver Assistance Systems) we are describing those features that assist the driver and need some kind of situational awareness: Position of the host vehicle within driving lane Existence of target vehicles in blind spot Detection of free parking slot and so on! To do this, we need sensors ADAS, in this context, does NOT refer to: Passive safety features! ABS / DSC Any features that only require ego vehicle state data 4

5 ADAS into Autonomy Looking at definitions put forward by bodies such as the SAE, it is clear that ADAS is considered as the first step towards highly automated driving features. Level Name Definition 0 No Automation the full-time performance by the human driver of all aspects of the dynamic driving task, even when enhanced by warning or intervention systems 1 Driver Assistance the driving mode-specific execution by a driver assistance system of either steering or acceleration/deceleration using information about the driving environment and with the expectation that the human driver perform all remaining aspects of the dynamic driving task 2 Partial Automation the driving mode-specific execution by one or more driver assistance systems of both steering and acceleration/deceleration using information about the driving environment and with the expectation that the human driver perform all remaining aspects of the dynamic driving task 3 Conditional Automation the driving mode-specific performance by an automated driving system of all aspects of the dynamic driving task with the expectation that the human driver will respond appropriately to a request to intervene 4 High Automation the driving mode-specific performance by an automated driving system of all aspects of the dynamic driving task, even if a human driver does not respond appropriately to a request to intervene 5 Full Automation the full-time performance by an automated driving system of all aspects of the dynamic driving task under all roadway and environmental conditions that can be managed by a human driver SAE Levels of Autonomy 5

6 Journey to intelligent driving 6

7 Journey to intelligent driving Fewer accidents better traffic flow and less pollution Jaguar Land Rover s direction Assist and enhance the driver offering several autonomy levels Autonomous car à takes away tedious parts of the journey while allowing the driver to do other things without removing the emotional connection with the car è A car able to drive itself if the driver chooses or one with systems that can be adjusted for a more engaging and safer drive 7

8 Journey to intelligent driving NCAP as a driver Junction AEB? Ped- AEB Cyclist - AEB LDW AEB TSR

9 Features and sensing

10 Features Traffic Sign Recognition Cross Traffic Alert ACC Night Vision Auto High Beam Magic Carpet Cruise Assist 3D image Lane Departure reconstruction Warning Foundations for Fwd. Collision Automatic Autonomous Warning Emerg. Driving Braking Applications! Lane Keep Assist Cross Traffic Auto Brake Blind Spot Monitor Active Blind Spot Top View Park Aid Junction View Safety related features Visualization enhancement Driving support 10

11 Current sensors 150m 100m 50m 0m - 50m - 100m Digital Map Surround Cams Front Radar Not to scale PDC Rear Radars Forward Looking Camera 11

12 Current sensors 150m 100m 50m 0m - 50m - 100m Digital Map Surround Cams Front Radar Not to scale PDC Rear Radars Forward Looking Camera 12

13 Importance of Image Processing Just as our current ADAS features can be considered as the foundations for our autonomous features, the camera and image processing should also be considered as the foundation for our sensing solution Vision systems provide an abundance of information similar to the human eye. Images contain a huge amount of information that we easily understand Geometry Surface type Recognition of objects/scenes Use of stereopsis, or stereo and structure by motion techniques, provides range/depth perception It is also a passive sensing method It will continue to be unaffected by other cameras, regardless of the number of vehicles on the road with the same sensing method can the same be said for Radar, Lidar and ultra-sonic technologies? 13

14 Importance of Image Processing 14

15 Importance of Image Processing Environment perception 3D environment map geometry Motion Context Traversability Obstacles Free path estimation Vehicle egomotion estimation Detecting and tracking of moving objects Road boundary Path following Physical terrain properties 15

16 Data Fusion Integration Callibration Medium Range Radar Long Range Radar Mono Camera Stereo Camera Simple Lidar Lidar Scanning Laser PDCs Surround Cams NIR FIR o o + o o o o - o o o + + Range o + Long. Performance Lat. Performance o Performance Cost Pros Bad weather o - o Low visibility Object classification o o Stationary objects Regulations o o o o o + Development TBD ++ + TBD TBD Piece + o Maturity o Good longitudinal performance - Mature solution - Range - Good longitudinal performance - Mature solution - Cost - Mature technology - Object classification - Lateral resolution - Improved longitudinal performance - Object classification, lateral resolution - Stationary object performance - Mature solution for some applications - Low cost - Very good lateral resolution - Field of view - Cheap, reliable solution for close field sensing - Complement for near field sensing - Natural display of images to driver - Cost in front of FIR - Improved performance of camera during night driving - Best detection of warm objects during night driving Cons - Lateral performance - Stationary objects - Lateral performance - Stationary objects - Longitudinal performance - Bad weather / low contrast performance - Bad weather / low contrast performance - Low range - Not a complete solution - Bad weather performance - Cost - Technology limitations - Availability of detection (bad weather / dirt) - Cost - Bad weather performance - Necessity of additional IR projector - Cost of the sensor - Only suitable for night vision & object detection 16

17 Other sensing and fusion As effective as cameras are, they have some drawbacks Limited range Performance is susceptible to common environmental conditions (rain, fog, varying lighting conditions) Range determination not as accurate as many active sensors The use of several sensor types can ensure a high level of confidence in target detection and characterisation Data/sensor fusion! At the beginning of this presentation, we said that ADAS (and by inference, Autonomous) features require situational awareness Sensing and sensor fusion is only a part of situational awareness 17

18 Bringing it all together

19 Situational Awareness & Sensing To create effective solutions for ADAS and Autonomy, we need to consider Situational Awareness What is Situational Awareness (SA)? SA is the perception of the elements in the environment within a volume of time and space, the comprehension of their meaning, and the projection of their state into the future. - Endsley, 1988 Endsley also defined a model for SA. This was reduced and adapted to enable the original definition to be operationalised. This is called the 3-Q (three question) model. 19

20 Situational Awareness & Sensing 20

21 Situational Awareness & Sensing These 3 questions can provide a foundation to describe, define and, more importantly, test and validate any vehicle perception use case. Note: The Who in WHO IS WHERE? also includes inanimate objects and environment features. The 3-Q Model can be applied to any any activity where the situation is in constant change, and where all data, evaluations and assessments (answers to the Three Questions) are of a transitory nature. Military research (Mavor et al 1995) suggested that SA be divided Local Awareness & Global Awareness Other papers (Andre et al 2005) split awareness three ways. The 3-Q model can be expanded into three types of awareness Transitory Awareness, Local Awareness, Global Awareness. Multiple awareness types may be being accessed at the same time 21

22 Situational Awareness & Sensing Transitory Awareness Local Awareness Global Awareness 22

23 Situational Awareness & Sensing Absolute relationship between objects Object position relative to world map - e.g. WGS coordinate system position for all objects Updated from local awareness and external sources (cloud, mapping companies, etc.) Expected to change slowly - Coventry isn t going to move very much! Mainly used for vehicle route planning and manoeuvres in a known space Global Awareness 23

24 Situational Awareness & Sensing Relative relationship between objects and the ego (host) vehicle Map origin centred on the ego vehicle - detected objects & features referenced relative the the car, not the world map Local Awareness Information exchanged between Global and Local awareness - e.g. location and characteristics of potholes, bumps, roundabouts, junctions, etc Data expected to change quickly - car still operating in a proactive rather than reactive manner Used to classify and characterise objects based on both behaviour and sensor return data 24

25 JLR Automotive Usage Situational Awareness & Sensing Transitory Awareness Transitory Awareness Instantaneous distance and bearing to objects Minimal/rapid characterisation based predominantly on sensor return, not object behaviour Object position data referenced to the sensor or the ego vehicle Rapid data updates Mainly used by reactive, not proactive features 25

26 Situational Awareness & Sensing Principles of Situational Awareness when used with use-case and scenario modelling can be used to derive perception requirements for ADAS and autonomous features Different use-cases may require different perception models Data fusion requirements may be based on scenario situation Perception needs in a car park are not the same as those on a motorway! As levels of autonomy increase so will the requirements of perception and situational awareness Human-like scenario context analysis may be necessary Ethics may drive complex awareness requirements ( Trolley Problem) 26

27 Conclusions ADAS and vehicle autonomy are being driven by safety as well as commercial reasons Jaguar Land Rover will be offering customer-selectable levels of assistance and autonomous driving features Current sensing and ADAS features are the foundation on which higher level, more complex features will be based The camera (machine vision) will the cornerstone of sensing Sensor/data fusion will be used to improve the sensing capabilities of Jaguar Land Rover vehicles A multi-tiered approach to Situational Awareness will drive better understanding of driving scenarios and lead to much more complete sensing and perception requirements. 27

28 THANK YOU! Any Questions? Contact Details:

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