Cellular-based Vehicle to Pedestrian (V2P) Adaptive Communication for Collision Avoidance

Transcription

1 Cellular-based Vehicle to Pedestrian (V2P) Adaptive Communication for Collision Avoidance Mehrdad Bagheri, Matti Siekkinen, Jukka K. Nurminen Aalto University - Department of Computer Science and Engineering - Espoo, Finland mehrdad.bagheri@aalto.fi, matti.siekkinen@aalto.fi, jukka.k.nurminen@aalto.fi Aalto University - Espoo, Finland ICCVE

2 Realizing V2P for Road Safety in Autonomous Cars Pedestrian detection and V2P communication A crucial requirement for future autonomous cars Also for advanced driver assistance systems Different scenarios for road safety, collision warning and collision avoidance Bushes Trees Parked Car Parked Car Aalto University - Espoo, Finland ICCVE

3 Cellular-based Active Road-safety Establish V2P communication, by means of smartphones, over cellular network. Integrate it with other detection methods. No need for new infrastructure Availability: for both vehicles (drivers) and pedestrians DSRC methods: still expensive, not deployed, not supported on phones Radar methods: dependent on line-of-sight Aalto University - Espoo, Finland ICCVE

4 Cellular-based Active Road-safety System Setup Network nodes: Vehicles Cyclists Pedestrians Network nodes beacon periodically Reporting their location, speed and direction Processing made to evaluate the risks Alert the car to brake Aalto University - Espoo, Finland ICCVE

5 Cellular-based Active Road-safety Deploy the road safety system Cloud back-end App on your mobile phone Aalto University - Espoo, Finland ICCVE

6 Energy Consumption Problem A typical (non-adaptive) road-safety system: Network nodes beaconing periodically For road safety, recommended 10 Hz frequency (100 ms interval) [1],[2] Frequent beaconing consumes a large amount of power [1] ETSI EN Intelligent Transport Systems (ITS); Communications Architecture. [Online]. Available: [2] ETSI TS , Intelligent Transport Systems (ITS); Vehicular Communications; Basic Set of Applications; Part 2: Specification of Cooperative Awareness Basic Service. [Online]. Available: Aalto University - Espoo, Finland ICCVE

7 Evaluate Negative Effect on Smartphone Battery Life Example: Messaging Frequency: 10 Hz (100 ms interval) Typical Battery Life = 16:00 Connection: LTE Battery Life: 04:39 (decreased more than 10 hours!) Battery life drops significantly Battery Life (hours) Deployment with such setup is NOT practical Note: Problem with all radio-based methods (DSRC, WiFi) 2 0 Typical Battery Life Running Road Safety App Aalto University - Espoo, Finland ICCVE

8 Adaptive Multi-level Communication Messaging Frequency: Adjusted according to collision risk Higher frequency Better precision and more reliability in prediction Higher frequency Worse battery life Try to minimize messaging frequency Messaging Frequency (Hz) Time no risk low risk high risk no risk low risk no risk Aalto University - Espoo, Finland ICCVE

9 Adaptive Multi-level Communication Simplified (Two Levels) Messaging Frequency: Adjusted according to collision risk Higher frequency Better precision and more reliability in prediction Higher frequency Worse battery life Try to minimize messaging frequency Messaging Frequency (Hz) Low-rate beaconing High-rate beaconing -- Time Low-rate beaconing no risk low risk high risk no risk low risk no risk Aalto University - Espoo, Finland ICCVE

10 How to Set Message Frequency? High risk level (full-rate mode): 10 Hz recommended Other risk levels (other modes): Messaging frequency is influenced by risk factors such as: Vehicle Speed Distance to Pedestrian And by other factors such as: Communication Latency Reaction Time Aalto University - Espoo, Finland ICCVE

11 How to Set Low-rate Message Frequency? Reaction + Brake: Min distance to alert the autonomous car Processing: Delay caused by communication (RTT) and computation Extra: chance to switch from low-rate to full-rate frequency higher speed shorter extra time need for a higher frequency Messaging Frequency (minimum possible) = f (relative speed, distance, cellular network specification) Aalto University - Espoo, Finland ICCVE

12 How to Set Low-rate Message Frequency? Messaging Frequency = f (relative speed, distance, cellular network specification) Example: Set frequency for different max. car speeds Distance = 200 meters Communication: LTE, RTT 50 ms Messaging Frequency (Hz) Car Speed (km/h) Aalto University - Espoo, Finland ICCVE

13 Calculate Energy Consumption Electrical power consumption of different risk levels (modes): Pfullrate and Plowrate Calculated based on models for 3G or LTE [1]. They depend on: Energy required to keep radio on Energy required to send one beacon message Do not depend on: Amount of data sent (beacon message has a tiny size) Total electrical energy consumed, depends on: Cellular network specification Beacon messaging frequency Fraction of time spent in risky situations: tfullrate vs. tlowrate Padaptive = ( Pfullrate tfullrate + Plowrate tlowrate ) / ( tfullrate + tlowrate ) [1] M. A. Hoque, M. Siekkinen, J. K. Nurminen, S. Tarkoma, and M. Aalto, Saving energy in mobile devices for on-demand multimedia streaming -- a cross-layer approach, ACM Transactions on Multimedia Computing, Communications, and Applications, vol. 10, no. 3, pp. 1 23, Apr Aalto University - Espoo, Finland ICCVE

14 Results with an Example Example Scenario: Battery Original Life-time = 16:00 Battery Capacity = 7.98 Wh Connection: LTE, RTT 50 ms Max Car Speed = 108 km/s (30 m/s) Time spent in risky situation: 1 hr (out of 24 hrs) Low-rate Frequency: 0.29 Hz (3.4 seconds interval) Battery life: 13:38 (decreased 02:22) Practical smartphone battery life Enables deployment of pedestrian safety systems Battery Life (hours) Non-adaptive Communication Typical Battery Life Adaptive Communication Aalto University - Espoo, Finland ICCVE

15 Results Summary Battery Life = f (relative speed, distance, cellular network, tfullrate / tlowrate) Battery life depends on risky situations encountered (e.g. during 24 hour) Many pedestrians spend a limited time in risky situations (e.g. less than 1 hour) Full-rate mode duration can be reduced even more with context-aware filters and optimization Practical smartphone battery life Enables deployment of pedestrian safety systems 1 Aalto University - Espoo, Finland ICCVE

16 Conclusion Dealt with limited energy source on smartphones Proposed adaptive multi-level method Results in practical energy consumption Enables deployment of pedestrian safety systems Aalto University - Espoo, Finland ICCVE

17 Discussion and Future Work Simulation Analyze following aspects Energy-efficiency of Different Smartphones Urban Areas and Pedestrian Daily Mobility Pattern Prototype Aalto University - Espoo, Finland ICCVE

18 Thank you! Any questions? Aalto University - Espoo, Finland ICCVE