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Safe and ecological speed profile planning algorithm for autonomous vehicles using a parametric multiobjective optimization procedure

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This slides have been presented in the Fast Zero 2017 conference in Nara, Japan. They describe how a multiobjective optimization procedure base on simulated annealing has been used to generate a speed profile for an autonomous vehicle. This speed profile is safe and ecological (minimizing energy use).

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Safe and ecological speed profile planning algorithm for autonomous vehicles using a parametric multiobjective optimization procedure

  1. 1. Institut français des sciences et technologies des transports, de l’aménagement et des réseaux www.ifsttar.fr Institut français des sciences et technologies des transports, de l’aménagement et des réseaux SAFE AND ECOLOGICAL SPEED PROFILE PLANNING ALGORITHM FOR AUTONOMOUS VEHICLES USING A PARAMETRIC MULTIOBJECTIVE OPTIMIZATION PROCEDURE Olivier Orfila Dominique Gruyer Karima Hamdi Sébastien Glaser COSYS-LIVIC
  2. 2. Institut français des sciences et technologies des transports, de l’aménagement et des réseaux www.ifsttar.fr Context: Energy and safety • Energy: • In France: passenger cars represents 55% of CO2 emissions • Regulations are stricts for all countries • Safety: • In France, 3469 people died on roads in 2016 • In EU, accidents targets will be difficult to reach
  3. 3. Institut français des sciences et technologies des transports, de l’aménagement et des réseaux www.ifsttar.fr Objective • Autonomous driving = miracle solution? • R&D on autonomous driving are concentrated on making it real (faisability) • Objective: • Develop an algorithm generating optimal speed profiles for automated vehicles: • Safety taken as constraints • Travel time and energy use as cost functions • Test it by comparing results to actual data
  4. 4. Institut français des sciences et technologies des transports, de l’aménagement et des réseaux www.ifsttar.fr Methodology • Proposing an algorithm managing several objectives: • MOSA (Multiobjective Optimization planning based on Simulated Annealing) • Testing its convergence and sensitivity to several parameters • Comparing it to experimental results on open road
  5. 5. Institut français des sciences et technologies des transports, de l’aménagement et des réseaux www.ifsttar.fr Proposed method properties • Chunked • Four profiles are defined • Multiobjective • Linear scalarisation • Optimal • Simulated annealing • Parametric • Combination of Intelligent Driver model segments
  6. 6. Institut français des sciences et technologies des transports, de l’aménagement et des réseaux www.ifsttar.fr Algorithm description • From an origin to a destination, extracting road data • From OSM (Open Street Map), curvature, slope, speed limit, junctions • Generating safety constraints on speed limits, safe curve speed and junctions • Chunking speed profile • Classifying each chunk • Optimizing speed on each chunk • Simulated annealing (*Kirkpatrick,1983) on a parametric speed profile • Each solution is a 5 elements vector describing a speed profile solution *Kirkpatrick, S.; Gelatt Jr, C. D.; Vecchi, M. P. "Optimization by Simulated Annealing". Science. 220 (4598): 671–680. (1983)
  7. 7. Institut français des sciences et technologies des transports, de l’aménagement et des réseaux www.ifsttar.fr Chunking • Constraints translated into square signal • Each chunk is identified depending on raising and falling edges
  8. 8. Institut français des sciences et technologies des transports, de l’aménagement et des réseaux www.ifsttar.fr Generating a chunk speed profile • Each chunk is associated to a speed profile parametric function depending on its type • From IDM* (Intelligent Driver Model) description of acceleration: • a: Initial acceleration (ms-2) • Vdes: desired speed (ms-1) • pa: percentage of distance of acceleration phase • pd: percentage of distance of deceleration phase • d: Final deceleration (ms-2) • Speed optimization using Simulated Annealing on these 5 parameters Speed limit Distance Speed Speed profile A profile Vdes pa pd *Treiber, Martin; Hennecke, Ansgar; Helbing, Dirk (2000), "Congested traffic states in empirical observations and microscopic simulations", Physical Review E, 62 (2): 1805–1824 Speed limit Distance Speed Speed profileB profile Vdes pa pd
  9. 9. Institut français des sciences et technologies des transports, de l’aménagement et des réseaux www.ifsttar.fr Simulated Annealing (SA) algorithm tests • Convergence • Before 500 of SA iterations • Sensitivity • High to initial values of SA parameters • Low to initial values of variables
  10. 10. Institut français des sciences et technologies des transports, de l’aménagement et des réseaux www.ifsttar.fr Selecting a multiobjective optimal solution • Challenge: • Find an optimal solution with two or more competing objectives • A solution: • Using preference based (linear scalairisation) combining two objective functions (*Deb, 2002) • Objectives: • Travel time • Fuel use min{𝑓1 𝑥 , 𝑓2 𝑥 } 𝑥 ∈ Ω, 𝑓0(𝑥) = 𝛼1 𝑓1 𝑥 + 𝛼2 𝑓2 𝑥 𝑑𝐸𝑡ℎ𝑒𝑜 = 1 2 𝜌 𝑎𝑖𝑟 𝑆𝐶 𝑥 𝑣2 + 𝐶𝑟𝑟 𝑚𝑔 + 𝑚𝑝 + 𝑚𝑎 𝑣𝑑𝑡, η = 𝐸𝑡ℎ𝑒𝑜 𝐸 𝑚𝑒𝑎𝑛 = 105 1 2 𝜌 𝑎𝑖𝑟 𝑆𝐶 𝑥 𝑣2 + 𝐶𝑟𝑟 𝑚𝑔 + 𝑚𝑝 𝑓(𝑣)𝑒 𝑐𝑎𝑟𝑏 𝜌𝑐𝑎𝑟𝑏 , *Kalyanmoy Deb, Multi-objective optimization using evolutionnary algorithms, Wiley, ISBN 0-471-87339-X, (2002)
  11. 11. Institut français des sciences et technologies des transports, de l’aménagement et des réseaux www.ifsttar.fr Comparing to test site experiments: Description of dataset • Experimental setup • A panel of drivers drove twice on the same road with the same car (normal and ecodriving conditions) • Test route • Test vehicle • Test participants • 21 drivers (40% female drivers) • Ecodriving advice provided orally before test drive
  12. 12. Institut français des sciences et technologies des transports, de l’aménagement et des réseaux www.ifsttar.fr Results • Global results • Several speed profiles generated • They repect safety rules (curve and limit speeds) • Pareto plot • MOSA better than all experiments on energy use • MOSA performing better than Dijkstra • MOSA cannot reach some points (travel time)
  13. 13. Institut français des sciences et technologies des transports, de l’aménagement et des réseaux www.ifsttar.fr Conclusions • Conclusions: • A multiobjective speed profile optimization algorithm has been proposed, tested and compared to experiments • The proposed method (MOSA) seems to outperform experimental results and previous method in given conditions • Perspectives: • MOSA is being implemented in electric autonomous vehicle (with VEDECOM) • MOSA can be implemented onboard as assistance system for curve warning and energy efficient speed advice (ecodriving) • Need to be extended to dynamic obstacles (reactive system)
  14. 14. Institut français des sciences et technologies des transports, de l’aménagement et des réseaux www.ifsttar.fr Thanks for your attention Olivier ORFILA Researcher IFSTTAR Deputy director LIVIC olivier.orfila@ifsttar.fr www.olivierorfila.fr Tél. +33 (0)1 30 84 40 25 LIVIC - Laboratoire sur les Interactions Véhicules-Infrastructure-Conducteurs 25 allée des Marronniers 78000 Versailles FRANCE

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