1. Radar-MAC: Mitigating Radar Interference in Self-Driving Cars
IEEE SECON, June 2016
London, UK
Radar-MAC: Mitigating Radar Interference in Self-Driving Cars
I'm gonna talk today about the work we did with Google a year ago on their self driving car. This was just a 3 months long project but very real and exciting.
We will have 10 min of questions but also please stop me if u don't feel like waiting till the end.
Dan McCloskey, Russell Smith, and Timothy Campbell
Google Inc.
Mountain View, CA, USA
Joud Khoury (jkhoury at bbn.com)
and Ram Ramanathan
Raytheon BBN Technologies
Cambridge, MA, USA

2. Problem
Today, radar parameters such as frequency/time offsets are assigned in an uncoordinated manner (at random)
In dense environments, radars get blind (signal quality drops below acceptable threshold) due to interference from other radars

3. It gets bad 30 total offsets to allocate
Uncoordinated assignment results in radar blindness when as little as 4 vehicles are within transmission range
Number of conflicting assignments increases super-linearly with the number of vehicles
We need algorithms to dynamically assign radar parameters to mitigate interference using the least amount of spectrum

4. RadarMAC: System Overview2
Process position, orientation, and trajectory for all vehicles
Compute position, orientation, trajectory and send to DC periodically 1
Dispatch Center (DC)
Vehicles
Planning Period
P = 1 sec 7
Assign and send playbooks to radars … 3
Every planning period T
Create interference graph for Q snapshots
into the future, at t=ti+kΔ for k=1 to Q
G(t=2)
G(t=3)
G(t=4)
Period 1
t1=1
t=1 …
Epoch Δ = 1 sec
Num Epochs in Horizon Q = 3 6
Map colors to radar parameters
f1 MHz
f2 MHz
f3 MHz 5
Color smG 4
Create smashed graph smG

5. Significant improvement over baseline
Baseline – uncoordinated
RadarMAC

6. FMCW Radar Degrees of Freedom
4 radars per vehicle to cover 360 degree field-of-view
Radar parameters: frequency offset f0, time offset t0, ramp range ΔF, transmit power Pt, and duty cycle d
Goal is to pack as many ramps in frequency and time within the B MHz while minimizing interference

7. Revisit Dispatch Center Loop
Epoch Δ = 1 sec
Num Epochs in Horizon Q = 3
t=0
G(t=1)
G(t=2)
G(t=3)
Period 0
t0=0
Future time
Planning Period P = 1 sec
t=1
G(t=2)
G(t=3)
G(t=4)
Period 1
t1=1 …
Future snapshots computed based on trajectory info
Actual time
Smashed Graph G(t=2..4)
Compute Q=3 snapshots and interference graphs G(t)
Smash (union) G’s to get smashed graph G(t=2..4)
Color and assign parameters to G(t=2..4)
Send parameters to vehicles to use for next 3 seconds
Guaranteed non-blindness for next 3 seconds

8. Algorithm: Compute Interference Graph
Vehicles …
Interference/
Conflict Graph
Node=radar
Edge=conflict
How do we account for the aggregation effects using only edges? B3 C3 B2 C2 B0 B1 C0 C1
SIR-1A0,j 10 7 1
0.04
0.02
0.01
10-4 1 3 2 C A B 10 7
0.04 A0 B0 B1 B2 B3 C0 C1 C2 C3
Subgraph of radar A0
The question is how do you account for the aggregation effect using only edges?
Reduced the problem into a linear combination of pairwise interference edges: an edge
To choose the subset of nodes to create edges to, we sort and find a threshold point
Red lines result in edges in the conflict graph G
Coloring G eliminates the red lines & guarantees no blind radars

9. Algorithm: Color Interference Graph
Smashed conflict graph
Color
Given the conflict graph G, color G using the minimum number of colors so as to eliminate conflicts
Any two nodes with an edge must have different colors (distance-1 constraint)
Coloring with distance-1 constraint is NP-hard
Greedy Coloring: deterministic and has good efficiency properties (number of colors used)
Baseline Random Coloring: chooses a random color from a fixed pool, colors node only once

10. Algorithm: Parameter Assignment
f1 MHz
f2 MHz
f3 MHz
Conflict graph colored using C total colors
Map colors to radar parameters
Strict preference ordering
Wideband frequency offsets when C <= Nw
Narrowband frequency offsets when Nw < C <= Nn
Wideband frequency & time offsets when Nn < C <= Nw+Tw
Narrowband time offset when Nw+Tw < C <= Nn+Tn
Use (4) and assign at random for colors > Nn+Tn
where N is number of freq offsets, T is number of time offsets

11. Takeaways: Safety
Eliminate blinds when frequency and time offsets are available
Significantly outperform baseline in terms of fraction of blinds
Good spatial reuse: sparse scenario shows reuse factor of ≈ 4
Robust to LTE link latency and loss
Dense
Sparse
>25% blinds for baseline
>40% blinds for baseline
runs out of offsets
(time and frequency)
Enough offsets
zero blinds
zero blinds

12. Takeaways: Spectrum Utilization
Significantly outperform baseline in terms of Spectrum Utilization
Baseline has no spatial reuse
Good spatial reuse in sparse scenario
Dense
Sparse
80% at aV=10
80% at aV=20
80% at aV=10
80% at aV=60
runs out of freq offsets
runs out of freq offsets

13. Takeaways: Actual Running Time
The actual running time of the SIR graph creation algorithm dominates the processing time
Expensive running time: ~1 sec for n=400 (aV=100)
The constants matter!
SIR Interference Graph creation time
Qn2logn complexity
n = 4.aV
*2.6 GHz Intel core i7

14. The algorithms work: they correctly and efficiently
Summary / Takeaway
The algorithms work: they correctly and efficiently
allocate offsets to eliminate interference
no blinds for up to 46 vehicles in the dense scenario (23 narrowband frequency offsets + 23 time offsets)
reuse offsets spatially for efficiency
no blinds for up to 190 vehicles in the sparse scenario (factor ~4 reuse)
Several topics for future work to enhance spectrum efficiency, and safety, and computation speedups

15. Questions?
Photograph by Mary Evans/Walt Disney Pictures/Ronald Grant via Everett Collection