Safety-Related Developments in Advanced Driver Assistance

1. Safety-Related Developments in Advanced Driver Assistance Environmental Perception & Cooperative Driving Jeroen Ploeg TNO Technical Sciences

2. Outline • Introduction • Trends in Advanced Driver Assistance • Collision mitigation & avoidance • Probabilistic Risk Estimation for Vulnerable Road Users • Cooperative Driving • Cooperative Adaptive Cruise Control • Conclusions

3. Societal Trends • A growing need for mobility, individuality, freedom • Expected growth in mobility 20 – 30% in the coming decade (in the Netherlands) • Consequence 1: current safety level will be hard to maintain • Consequence 2: “vehicle loss hours” will significantly increase • Consequence 3: emission level/fuel consumption increases • Significantly more road space in the living environment is not acceptable • Advanced Driver Assistance (ADA) offers possibilities ...

4. Advanced Driver Assistance (ADA) • Advanced Driver Assistance (ADA) systems • “systems that support the driver in his driving task, primarily based on information regarding the local traffic situation” • Vehicle dynamics systems excluded, such as • ABS • ESP • ...

5. controllability: autonomous driving • Cooperative Driving results in2: • 50% less traffic congestion • 8% less traffic accidents • 5% less CO2 emission • VRU safety1: • 43% road fatalities are pedestrians • 5% cyclists mobility: cooperative driving safety: collision warning mitigation  avoidance comfort: cruise control, advanced cruise control 1TETSC PIN annual report 2009 2TNO report 2008-D-R0996/A: “Smarter and better – the benefits of intelligent traffic” ADA trends

6. Number of road fatalities in the Netherlands Rijkswaterstaat, Kerncijfers Verkeersveiligheid, www.rijkswaterstaat.nl/dvs, 2009 ADA trend 1: Vulnerable Road Users Total: decreasing number of fatalities VRUs: 318 fatalities out of 750 total in 2008 (> 42%)

7. ADA trend 2: Cooperative Driving • Cooperative Driving • Influencing the individual vehicles, either through advisory or autonomous actions, so as to optimize the collective behavior with respect to: • Safety (also affects throughput) • Throughput (highways + urban roads) • Emission/fuel consumption (trucks) • Main enabler: wireless communications

8. Risk estimation for VRUs • Injury reduction • Driver warning • Autonomous braking • Airbag deployment to reduce impact Environmental perception for VRU Sensor fusion Risk estimation Sensor Driver warning Sensor Image processing Clustering, assignment Sensor fusion Object identification Risk estimation Autonomous action Airbag deployment eCall Assessment (driving & pre-crash & crash)

9. Vehicle risk estimation • Predict trajectories of detected objects (vehicles) and host vehicle across a certain time-horizon • Quantify optimal trajectory by use of a cost function • Collision-free -> keep safe distance to obstructing objects • Feasible trajectory -> low accelerations • Minimize the cost function by choosing optimal host vehicle trajectory

10. no collision collision Vehicle risk estimation (cnt’d) object safety distance predicted object trajectory host minimum distance predicted host trajectory

11. P P Risk estimation for VRUs – main principle • VRUs behave rather non-deterministic • Probabilistic approach is proposed to cover the resulting uncertainty in the path prediction of VRUs • Assumption: object classification is known to choose the correct PDF Probability Density Functions (PDF) Probabilistic Risk Estimation

12. Implementation • At time t = t0 the position, orientation, velocity, (rotational velocity, acceleration) are known of the detected object(s) and own vehicle • Determine position probability of object over a certain time horizon • Determine maximum `overlap’ of host and object probability density function  collision probability

13. Simulation • Here, normal distributions are chosen for: • forward velocity • heading

14. Experiments Collision probability [%] Time-to-collision [s] MIO index Time [s]

15. Summary risk estimation for VRUs • Probabilistic Risk Estimation (PRE) provides an estimation of the collision probability in the presence of large uncertainties with respect to future object behavior (such as with VRUs) • Modular, generic approach, serving multiple ADAS applications • object detection & classification • prediction & risk estimation • Liability issue: will the driver remain responsible?

16. Cooperative Driving • Two types of systems, roughly • Warning/advisory systems  not time-critical event-triggered • Automatic systems  time-critical time-triggered real-time closed loop control • Cooperative Adaptive Cruise Control (CACC) • Basis: Adaptive Cruise Control (ACC)+wireless communication • Vehicle-following control objective • Increase safety by automatically“smoothing” traffic throughshockwave mitigation

17. String stability – human driving behavior • Sugiyama, Y.; Fukui, M.; Kikuchi, M.; Hasebe, K.; Nakayama, A.; Nishinari, K.; Tadaki, S. & Yukawa, S., Traffic Jams without Bottlenecks - Experimental Evidence for the Physical Mechanism of the Formation of a Jam. New Journal of Physics, 2008, 10 (033001), 7

18. String stability – ACC • Infinite string • ACC, with time headwayh = 0.5 s • Initial velocity 72 km/h • Initial condition error ofone vehicle of 2 m • String unstable with linear controller, a collision occurs

19. String stability • Take 2nd-order systems in series connection  = 1.1  = 0.73  = 0.5

20. String stability – conditions • Define String Stability Complementary Sensitivity i(s) such thatwith inverse Laplace transform i(t) (impulse response function) • Then, from linear system theoryknown as the L2 gain, andi.e., the L gain.

21. String stability – conditions (cnt’d) • Hence, in order to have disturbance attenuation in upstream direction, we requirei.e., L2 string stability, ori.e., L string stability

22. String stability – conditions (cnt’d) • 2nd-order systems in series connection string stable L2 string stable L string unstable string unstable

23. CACC design – communication topologies • Ad-hoc platooning: no designated platoon leader • Realistic solution for everyday traffic • Least demanding for communication • Unidirectional communication with directly preceding vehicle

24. CACC design – spacing policy • Spacing policyh: time headway [s]r: standstill distance [m]Spacing policy improves string stability properties! • Controller acts on vehicle acceleration to realize the desired spacing

25. CACC design – controller • Spacing policy transfer function: • Vehicle model G(s), communications time delay D(s), controller K(s)

26. CACC design – string stability • String stability complementary sensitivity • Hence, without communications delay • Consequently, L2 string stable (L string stable as well).

27. CACC design – simulation results • Without communication (h = 0.5 s) • With communication (h = 0.5 s)

28. CACC design – simulation results (cnt’d) • “platoon” of 8 vehicles, 1st vehicle introduces speed variations

29. CACC experiments • Test fleet: 7x Toyota Prius, equipped with • Wireless communications (IEEE 802.11g) • GPS • CACC control computer • Low-level vehicle controlcomputer (interacts withthe vehicle CAN bus toautomatically accelerate/decelerate)

30. CACC experiments (cnt’d) • Test fleet: 7x Toyota Prius (no. 7 is missing :-)

31. CACC experiments (cnt’d) • Lelystad, March 18, 2011

32. CACC experiments (cnt’d) • Velocity responses of test fleet ACC (i.e., no WiFi) CACC

33. CACC – object tracking • Objective • Determine relevant target vehicles based on multiple sensors, s.a. wireless comm. (802.11p) and radar • Or, in other words: • match radar data with WiFi data • fuse data to get reliable object motion data • Packet-loss & inaccuracy of communicated GPS data biggestchallenge

34. CACC – object tracking (cnt’d) Pre- processing Feature Filtering Data Clustering Object State Estimation Object Classification • “Raw” measurement data • Radar • Range, bearing, range rate • Relative to host vehicle • Wireless Communication • Position, velocity, acceleration • Absolute coordinates • Coordinate transformations to make data comparable • Basic data acceptance/rejection • Ignore objects driving in opposite direction • Set-up object data matrix • Define Region of Interest (ROI) for host vehicle, based on: • Host vehicle motion (kinematic) • Application specific • Reject data outside ROI • n = 6 Kalman filter groups • Each group contains m Kalman filters • Each Kalman filter suits a specific data combination • WiFi, radar, WiFi + radar m = 3 • Total n·m = 18 filters • Cluster data of different sources according to (expected) objects • Method: Quality Threshold clustering • Based on distance • Assign clusters to Kalman filter objects • Activate, reset, de-activate filters • max. n objects (n = 6, currently) • Application specific object classification • Most Important Object(s) (MIO) • Bidirectional CACC • Forward MIO • Backward MIO • Relevant objects • Motion data “as good as possible” • Not good enough: graceful degradation • to be judged on controller level • to be implemented on controller level • Application specific! • Reliability measure • Estimation error covariance per object

35. CACC – object tracking (cnt’d) Results • Simulated scenario • 6 vehicles • Wireless comm. + radar • Forward MIO & backward MIOtracking

36. CACC – object tracking (cnt’d) Results (cnt’d) • Measurements • Real-time implementation • Prius radar measurements • No wireless comm. yet • Forward MIO tracking

37. CACC – object tracking (cnt’d) Results (cnt’d) • Measurements • Real-timeimplementation • Prius radar measurements • No wirelesscomm. yet • Forward MIOtracking

38. CACC – object tracking (cnt’d) Results (cnt’d) • Measurements • Real-timeimplementation • Prius radar measurements • No wirelesscomm. yet • Forward MIOtracking

39. Summary CACC • CACC enables automatic smoothing of traffic through enforcement of string stable behavior  increases safety by decreasing the number of potentially dangerous events • Design focusing on implementation is feasible • CACC can be regarded as add-on to ACC • Standardization in wireless communications well under way (IEEE 802.11p, ETSI Geo-routing & message content) • Object tracking is a generic component (also used in VRU safety)

40. Conclusions • Advanced Driver Assistance: • Increased focus on VRU safety • Increased focus on Cooperative Driving (wireless communications) • Both types, although very different by nature, rely to a large extend on detection, estimation & classification of road users • Both types are time- & safety-critical and even automatic • Changing the role of the driver from “real-time controller” to “supervisor” opens up a whole new perspective with respect to improving traffic safety.