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| Date post: | 31-Dec-2015 |
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Outline
• Introduction for Automatic Vehicles from Technical Aspect
• Proposal of My Master Thesis– Optimized Lane Assignment in Urban environment
History
• Development of Driver Assistant Systems– Step by step introduction from driver assistance
• Requirement• Technical feasibility• Acceptance
– ABS has already take over the driver’s control• ABS 1978• ESP 1995• PRE-SAFE 2002
Current Driver Assistant Systems
• PRE-SAFE – two versions– From 2005, constituted in an S-Class (W221)– Combination with Distronic – Distance Keeping with Radar– Capture possible collision accidents through comparison between the
speeds of each vehicles
– Reaction in steps, timely depend on computed accident time points1. (t-2.6s): acoustic/optic warning to the driver2. (t-1.6s): automatic braking with 40% brake power3. (t-0.6s): full brake power – driver can not take over the system4. (t-0.1s): activate the safety belt, seat control, head support, windows…
Research Towards Total Automatic Vehicles
• Prometheus Project (1985-1997)– The biggest European research project for automatic vehicles (till
now)– Highway/freeway as goal area, human driver for safe concern– Up to 1700km distance, up to 180 km/h, longest distance without
human control 158 km
Research Towards Total Automatic Vehicles
• DRAPA Grand Challenges– DARPA = Defense Advanced Research Projects Agency– Military applications from automatic vehicles (e.g. exploration,
search and rescue, wardership)
– Grand Challenge I – 2004• 240km Off-Road• 10 Hours • Total automatic navigation• 1 Million $ Prize• 15 participants – mostly US University• No Team has reached the Goal• The best team went 12 KM
Research Towards Total Automatic Vehicles
• DRAPA Grand Challenges– Grand Challenge II – 2005
• 240km Off-Road• 10 Hours • Total automatic navigation• 1 Million $ Prize• 23 participants – mostly US University• 5 Teams have reached the Goal• 22 teams has surpassed best of the last year
• Winner (Stanford) has finished the 240km in 6 hours and 54 minutes (~34km/h)
Research Towards Total Automatic Vehicles
• DRAPA Grand Challenges– Grand Challenge III – 2007
• 100km on a modified street system• 6 hours• Total automatic navigation• Following the traffic rules• 1 Million $ Prize• 35 participants includes international competitors
• 6 Teams have reached the Goal
Research Towards Total Automatic Vehicles
• PATH– The California Program on Advanced Technology for the Highway
(1986 - Now)
– Traffic Operations Research• Traffic management• Traveler information
– Transportation Safety Research– Modal Applications Research
Used Driving Areas
• Highway (Prometheus)• Desert/Off-Road (DARPA I&II)• Modified City-Scenario (DARPA III)
– Clear defined environment– Countable situations– Clearly recognizable environment
• Other situations also imaginable, e.g. car park
Expected Driving Areas• Where the drivers want:
– Boredom– Overextension
• Where the lives can be protected:– In serious dangerous situation– Seconds before a accident
• Where human is in continuing danger – Crisis area– Catastrophic area
• Earthquake• Radiate area
Definition• A Vehicle, which automatic drives and
– Goes after the Goal complying the rules– Perceives, interprets the environment and reaction in an appropriable way
Sensors
Goals
Rules
Sensor Evaluation
Strategy
Tactic
Actuators
World Model
Definition• Communication is the Key of effective automatic driving
Sensors
Goals
Rules
Sensor Evaluation
Strategy
Tactic
Actuators
World Model
Sensor-Sharing
Cooperative Route Optimization
Dynamic Distributed Maps
Cooperative Driver Maneuver
Sensors• Input for Situation Analyze• Transfer raw data• Generally:
– Radar (near/far)– Infra– Ultrasound– Image level
• Camera• IR-Camera (night sight)• Stereo-Camera (for 3D information)
– GPS• We also have:
– LIDAR (Laser scanner)• Punctual environment information
Sensors
Goals
Rules
Sensor Evaluation
Strategy
Tactic
Actuators
World Model
Sensor Evaluation
• Sensors collect only raw data– Pixel from Cameras– Voltage from IR/Ultrasound– Distance from Radar– 3D Pixel from LIDAR
• These Data must be evaluated– Pixel-Clustering– Object recognition/identification– Muster recognition
Sensors
Goals
Rules
Sensor Evaluation
Strategy
Tactic
Actuators
World Model
World Model
• Combination and Summary of the evaluated Sensor data• Situation Analyze• Compare with the previous data• Translate as real word • Categorizing in related concepts
Sensors
Goals
Rules
Sensor Evaluation
Strategy
Tactic
Actuators
World Model
Strategy
• Long-term plan of the route• Similar to navigation system• Updates in real time• Translate e.g. through Rule-Engine
Sensors
Goals
Rules
Sensor Evaluation
Strategy
Tactic
Actuators
World Model
Tactic
• Short-term plan• Transfer the Strategy to effective route plan• Change the lane plan according to situations
– Normal driving– Overtaking control– Avoidance control– Turn off procedure
• Control and correction through actuators according to new sensor condition (loop control)
Sensors
Goals
Rules
Sensor Evaluation
Strategy
Tactic
Actuators
World Model
Goals
• Pretended Goals of the Vechicle• For example
– Drive a circuit– Drive to Peking– Optimize the fuel consumption – Avoid traffic jam
• Impact on strategy and sometime also on tactic decisions
Sensors
Goals
Rules
Sensor Evaluation
Strategy
Tactic
Actuators
World Model
Rule
• Rules, which the vehicle must follow• Street traffic rules• Test restrictions• Can be overrode: avoiding accident with human is more
important than stay in the lane• Impact on tactic and sometime also on strategy decisions
Sensors
Goals
Rules
Sensor Evaluation
Strategy
Tactic
Actuators
World Model
Actuators
• Control the vehicle• Gas/Brake• Steering• Blinker• Cockpit-Electronic• Drive-by-Wire• Additional actuators (e.g. warn siren)
Sensors
Goals
Rules
Sensor Evaluation
Strategy
Tactic
Actuators
World Model
CommunicationSensor-Sharing
Cooperative Route Optimization
Dynamic Distributed Maps
Cooperative Driver Maneuver
(Evaluated) Sensor data share with other vehicles in the environment – position, speed and direction
Cooperatively route plan – avoiding traffic congestion, environment pressure, shorten the drive time
Broadened sensor sharing – part of the reconstructed informationAll the vehicles in a specific area share a local map with dynamic information
Cooperatively lane plan to avoid accidents and increase the traffic efficiency
Outlook
• Research has solved part of the full automatic driving problem• Further work needed for sensor evaluation and lane planning• Extend the communication between full automatic and
human controlled vehicles• Deploy the automatic vehicles in restricted areas
– Highway– Garage– In dangerous situations
• For “real” full automatic vehicles, we need not only research, but also impulse from politic, the vehicle manufactures and making dedicated laws
Proposal of Master Thesis
• Optimized Lane Assignment in Urban Environment– Goal– Scope– State of the art– Assumptions– Challenges
Goal
• Build a centralized control system that assign an appropriate lane for each vehicle on each road
• Provide a navigation algorithm to cooperate with the lane allocation to achieve a maximal throughput using the existent facilities
Scope• Construct a centralized control system to provide the
lane assignment for each vehicle• Provide lane assignment algorithms in dynamic urban
area and evaluate with simulation• Provide advanced navigation/routing algorithms to
utilize the benefits from the lane assignment• Evaluate the system with certain metrics to prove the
improvement of the global traffic efficiency • Out of Scope– Vehicle Maneuver (lane change/keeping)– Coordination between vehicles
State of the art
• Lane assignment is well studied under highway/freeway scenario– Platooning– Economic methods
• Comprehensive Vehicle Models• Dynamic routing algorithms
Main Assumptions• One general vehicle model
– Different vehicle model for different vehicle types: car, bus, truck…• 100% penetrate rate
– System behavior under different penetrate rate is important • Controller knows all the necessary information:
– For each vehicle: destination…– For each road: capacity, traffic…
• Unrestricted communication capability– Latency, capacity, overhead need to be take in consideration– Thus different communication technologies need to be utilized
• One central Controller– Using the intelligence of the vehicles
Challenges
• Modulation of the complex urban area– Different scenarios (single road, intersection, elevator…)– Micro & Macro scope
• Cooperate with other road facilities (traffic lights)• Metric of Efficiency• Fairness (who want to join the slow lane?)• Safety
