Simulation-based traffic management for autonomous and connected vehicles

Transcription

1 Simulation-based traffic management for autonomous and connected vehicles Paweł Gora Faculty of Mathematics, Informatics and Mechanics University of Warsaw ITS Kraków,

2 Axioms Vehicles may communicate with each other (V2V) and with infrastructure (V2I, I2V) Car s onboard computer may know : static components of the road network precise location and speed of the car GNSS data (GPS, Galileo + EGNOS) sensors: lidar, laser, camera, intended destination and route of the car similar dynamic attributes of other vehicles (thanks to V2V, V2I, I2V) and bikes / pedestrians from the vicinity (sensors, mobile devices) Traffic management center may know the static road network structure, locations, speeds, intended destinations and routes of all vehicles (V2I) All of these is technically feasible even nowadays!!!

3 Conventional vs autonomous vehicles Conventional vehicles are driven by people we cannot predict their behavior (computational irreducibility of human's brain). Autonomous vehicles are driven by a computer running (interactive) algorithm (computational model equivalent to Interactive Turing Machine). Microscopic models approximate drive in typical conditions. Microscopic models could determine (or approximate with good accuracy) drive in typical conditions. It's difficult to predict human's response to a given atypical situation (assuming we don't have accurate driver's psychological model). Machine's response to a given atypical situation could be computed (deterministic or stochastic algorithm). It is difficult to predict traffic state with sufficient accuracy when atypical situations occur. Traffic state could be approximated with a good accuracy when atypical situations occur (in a long-term, sensitive dependence on initial conditions may make it impossible).

4 Implications In the era of self-driving cars with V2X communication (which is expected to come...): O-D matrix will be given (almost) for free thanks to V2I communication. Microscopic traffic simulation models might be calibrated online. Traffic simulation could be run few orders of magnitude faster than realtime (High- Performance Computing clusters) and be very accurate. Short-time prediction could be very accurate and fast thanks to simulations. Current traffic state (positions, velocities, accelerations, routes of all cars) could be known by a traffic management center and updated frequently. It may be possible to evaluate large number of configurations of traffic management parameters (e.g., configurations of traffic signals) and choose the best one metaheuristics might be a good mathematical tool.

5 Simulation-based traffic management system Large-scale simulation Travel request TMC V2I I2V High-performance computing cluster + Apache Spark

6 Large-scale traffic simulation Details in my papers, e.g.,: P. Gora, 2009, Traffic Simulation Framework - a Cellular Automaton based tool for simulating and investigating real city traffic

7 Initial results Details are in my recent work ( ): Application of genetic algorithms and high-performance computing to the Traffic Signal Setting problem Assuming we have a complete knowledge about current positions, speeds and routes of cars (and O-D matrix to simulate new cars, starting drive within the next 10 minutes): I was running simulations (using TSF) to assess quality of traffic signal settings (offsets) Genetic algorithm encoding offsets to genotypes (population: genotypes, 10% best genotypes selected for reproduction) Simulating 10 minutes of large-scale traffic (with over cars) using microscopic models takes several minutes, using mesoscopic model a few seconds Genetic algorithm needs to assess many traffic signal settings to find good solutions, so: Mesoscopic model is better (faster, less accurate, but still can find acceptable solution) Parallelization of traffic signal settings evaluation is required (algorithm level, simulation level) Other metaheuristics (e.g. Monte Carlo methods, Metropolis-Hastings, simulated annealing)

8 Initial results Details are in my recent work ( ): Application of genetic algorithms and highperformance computing to the Traffic Signal Setting problem Experiments conducted from January to July 2015, High-Performance Computing cluster (University of Rzeszów): 40 computational nodes, 200 cores, computational power: 7.5 TeraFLOPS Up to 18.12% reduction of travel delay on the whole road network of Warsaw (~800 crossroad with traffic signals) Parallelization on Experiment level (running many instances of GA in parallel, on many cores) A single GA run: at least seconds ~= 3 hours the methodology cannot be applied to real-time traffic management yet, but... After introducing Algorithm level parallelization (evaluating TSS qualities in parallel within a single GA run is technically feasible) ~100 seconds < 2 minutes (might be applied to realtime traffic management After introducing Simulation level parallelization (running a single traffic simulation in parallel potentially could be technically feasible): HPC cluster in Rzeszów ~100 seconds (might be applied to real-time traffic management) Larger clusters (e.g., Prometheus in Kraków, 1.7 PetaFlops) a few seconds (or less)

9 Microscopic traffic simulation with autonomous and connected cars Inga Rüb, Idea: think of a single car as of an agent in a multiagent system (details in our paper: Traffic models for autonomous and connected cars - to be published in April 2016) BDI concept: Beliefs Desires knowledge of the environment (obstacles, other cars and their planned routes) general rules to follow and goals to obtain Actions: Intentions plans for the nearest future (the next simulation step) slow down, speed up, change direction, complex actions...

10 Issues The world is not ideal: Issue Bugs in code Defects of crucial hardware parts (e.g. sensors, communication devices, cars) Potential solution Comprehensive tests Redundant components and functionalities, self-diagnostics, selfhealing, self-repairing Cryptographic attacks Secure communication protocols, communication limited to simple, verificable and indispensable commands Coexistence of autonomous/connected and conventional vehicles Pedestrians, cyclists Bad weather conditions Detecting other cars using sensors, new microscopic models. Sensors scanning environment, detecting and predicting motion, communication with mobile devices, airbags V2X / I2V communication to improve detection

11 Conclusions Autonomous and connected cars may bring revolution in transportation and traffic management (much less accidents, better traffic management). New traffic models are required. Large-scale traffic simulation might be applied to real-time traffic management (e.g., (sub)optimal reaction to atypical conditions within a few seconds). High-performance computing clusters (e.g., PEGAZ in Rzeszów, Prometheus in Kraków) might be required (alternatives: GPU, quantum computer?). This is just initial idea (and initial results). My goal was to show that this might be technically feasible in the future, so some research efforts should be put on this direction. I assess the idea might be implemented in years.

12 Questions? Thank you!!! Logic will get you from A to B. Imagination will take you everywhere. A. Einstein Sky is not the limit. The presentation was partially supported by the research grant 2011/01/D/ST6/06981 of the National Science Centre