Principal Investigator

John Dolan

Status

Completed

Start Date

Jan. 1, 2015

End Date

Dec. 31, 2015

Project Type

Research Advanced

Grant Program

MAP-21 TSET - Tier 1 (2012 - 2016)

Grant Cycle

2015 TSET UTC

Visibility

Public

Abstract

Driver safety is aided by multiple driver assist systems in modern cars, such as anti-lock braking and lane departure warning systems. The prevention or amelioration of a certain class of accidents is dependent on  rapid  situation  assessment  and  resultant  highly  responsive  evasive  maneuver.  Increasingly  capable sensors   are   improving   situation   awareness.   However,   current driving   planners/controllers for autonomous  cars fail  to  generate  a  safe  plan,  particularly  in  emergency  situations.  The  main  reason  is that such planners  are  designed  to  produce  comfortable  trajectories  for  human  passengers  under normal   driving   conditions.   This   project   aims   to   detect   conditions   under   which   potentially   high-performance evasive maneuver is necessary to avoid collision, and to invoke a special planner/controller evasive maneuver mode in order to react safely to such conditions.    

Description

Problem statement
Driver safety is aided by multiple driver assist systems in modern cars, such as anti-lock braking and lane departure warning systems. The prevention or amelioration of a certain class of accidents is dependent on  rapid  situation  assessment  and  resultant  highly  responsive  evasive  maneuver. Increasingly capable sensors are   improving   situation   awareness.   However,   current   driving   planners/controllers for autonomous  cars fail  to  generate  a  safe  plan,  particularly  in  emergency  situations.  The  main  reason  is that such planners  are  designed  to  produce  comfortable  trajectories  for  human  passengers  under normal   driving   conditions.   This   project   aims   to   detect   conditions   under   which   potentially   high-performance evasive maneuver is necessary to avoid collision, and to invoke a special planner/controller evasive maneuver mode in order to react safely to such conditions.

Application
The  developed  methods  would  apply  and  could  be  invoked  whenever  emergency  conditions  are detected. In manual driving, this could occur if the driver’s attention is wandering, or his limited situation  awareness  is  not  sufficient  to  take  in  the  entirety  of a  dangerous  situation.  An  automatic evasive  maneuver  capability  in  that  case  would  be  like  an  extreme  extension  of  the  anti-lock  braking already  present  on  production  vehicles,  which  does  not  require explicit user  activation.  In  fully autonomous driving, the evasive maneuver capability would invoke extreme performance capabilities of the vehicle and its planner/controller not active under normal conditions.

Approach
The proposed work has two components: detection and planning/execution. The detection component will be dealt with at a simple level in the proposed work, though it is a complex topic in its own right and should be elaborated in a separated and related project. In the proposed work, detection will be based on the   combination   of   road   topology,   prediction   of   the   future   course   of   all   relevant   vehicles (probabilistic  where  multiple  road  topology  options  exist),  and  knowledge  of  human  response  times. Issues such as focusing of sensor attention and driver attention/gaze monitoring should be considered in separate work, but we will neglect them. Given our simplifying assumptions, we will develop techniques for the avoidance of false positives in determining the necessity for evasive maneuver.
The  main  work  will  be  in  the  planning/execution  area. The  typical  autonomous  driving  framework  is hierarchical,  with  the  following  general  levels:  high-level  reasoning,  motion  planning,  tracking  control, and actuation control. This framework can introduce significant delays in execution which are tolerable in  normal driving,  but  not  in high-performance  evasive  maneuver. We  will perform more  sophisticated planning-control integration using a model-based planning/control scheme by performing the following tasks:
1. Abstract away "tracking control" and let the output of the motion planning directly control the actuation, removing the lag involved in an extra tracking control layer.
2. To achieve that, account in the planner for a more realistic system model (actuation model + vehicle model).
3. Possibly add GPU acceleration in order to allow the planner to consider more trajectory options in real time.

Validation 
Initial  validation  will  be  performed  by  devising  a  small  set  of  canonical  scenarios  that  reflect  the  most frequent accident circumstances, which occur at intersections. These will be exhaustively investigated in simulation. They will then be validated in indoor tests on a small-scale real-world vehicle, a RC car. The car’s dynamics will be modeled and included in the planning/control loop.
Realistic  testing  of  autonomous  evasive  maneuver  on  an  actual  vehicle  is  challenging  for  multiple reasons: 1) a vehicle retrofitted for autonomy is needed; 2) fully authentic testing requires the creation of  dangerous  situations;  3)  such  testing  has  to  be  conducted  at  least  initially  on  a  controlled,  closed course.We  have  access  to  an  autonomous  vehicle  in  the  form of CMU’s autonomous  Cadillac  SRX. We will attempt to devise low-speed scenarios allowing validation of the basic approach. It may be possible to  scale  the  results  for  partial  validation,  but  high-speed  vehicle  dynamics  will  not  come  into  play  for safety reasons and will only be able to be validated in highly controlled test situations (see Deployment Plan below). 

Timeline

Duration: 1 year (Jan. 1 –Dec. 31, 2015)High-level schedule:Mar. 31: Architecture and theoretical developmentJune 30: Validation in softwareSept. 30: Validation on indoor hardware (RC car)Nov. 31: Validation on SRXDec. 31: Final report, documentation of hardware/software

Strategic Description / RD&T

Deployment Plan

Even  the  deployment  of  something  as  relatively  innocuous  as  anti-lock  braking  systems  faced  some opposition  in  the  form  of  user  discomfortat  not  being  in  full  control  of  the  vehicle.  This  applies  much more strongly to a capability as invasive of a driver’s control prerogatives as autonomous evasive maneuver.  The  best  path  for  deployment  is  through  our  research  sponsor  and  partner  GM.  They  have the controlled  testing facilities and procedures that allow rigorous and maximally safe testing of such a capability.

The deployment plan is therefore to:
1. Validate a basic capability to perform autonomous evasive maneuver during the course of the proposed project.
2. Begin   to transition   this   capability to   GM   R&D through   the   technology transfer mechanism   already   in   place   via   the   GM-CMU   Autonomous   Driving   Collaborative Research Lab (AD-CRL).
3. The technology can then be matured through GM’s proven processes to the point that it may become suitable for deployment on passenger vehicles.

Expected Outcomes/Impacts

Accomplishments
- Creation of a novel high-performance evasive maneuver planner
- Validation  of  the  planner  in  simulation,  on  a  small-scale  vehicle,  and  partially  on  a  full-scale vehicle
- Documentation  of  the  results  in  a form  appropriate  for  hand-off  to  a  partner  capable  of full-scale deployment

Metrics
- Ability to deal with 90% of the most frequent avoidable accident scenarios
- Reduction of total planning latency to a value at least comparable to, if not smaller than, human reaction times (subsequent work should reduce it further)

Expected Outputs

TRID

Individuals Involved

Email	Name	Affiliation	Role	Position
jmd@cs.cmu.edu	Dolan, John	Robotics Institute	PI	Faculty - Research/Systems
tianyu@cmu.edu	Gu, Tianyu	ECE	Other	Student - Masters

Budget

Amount of UTC Funds Awarded

$74416.00

Total Project Budget (from all funding sources)

$74416.00

Documents

Type	Name	Uploaded
Final Report	2015_Dolan_-_UTC_Final_Report.pdf	May 11, 2018, 4:19 a.m.
Publication	Human-like planning of swerve maneuvers for autonomous vehicles	April 19, 2021, 6:11 a.m.
Publication	Automated tactical maneuver discovery, reasoning and trajectory planning for autonomous driving	April 19, 2021, 6:12 a.m.
Publication	Safe Planning for Self-Driving Via Adaptive Constrained ILQR	April 19, 2021, 6:14 a.m.
Publication	Safe planning and control under uncertainty for self-driving	April 19, 2021, 6:15 a.m.

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