The U.S. Department of Transportation (U.S. DOT) announced the winners
of the U.S. DOT Intersection Safety Challenge Stage 1B: System Assessment
and Virtual Testing Primary Track at the 2025 Transportation Research Board (TRB) Annual Meeting.
The purpose of the Intersection Safety Challenge, a multi-stage prize competition, is
to encourage teams of innovators and end-users to develop, prototype and
test intersection safety systems (ISS) that leverage emerging technologies
including artificial intelligence (AI) and machine learning (ML) to identify
and mitigate unsafe conditions involving vehicles and vulnerable road users
at roadway intersections. The Challenge draws on the expertise of researchers
and practitioners from across the United States. This includes universities, State
and local agencies, private sector developers, and other organizations.
In Stage 1A, participants submitted design concepts for their proposed intersection safety systems.
Winners from Stage 1A were invited to participate in the Stage 1B Primary Track where they tackled
a series of technical challenges—including sensor fusion, classification, path and
conflict prediction—utilizing U.S. DOT-provided real world sensor data collected on a closed course
at the Federal Highway Administration (FHWA) Turner-Fairbank Highway Research Center (TFHRC).
For the Stage 1B Primary Track, U.S. DOT awarded 10 teams prize amounts ranging from $166,666 to $750,000
across two Tiers, for a total of $4,000,000 in prize awards. Tier 1 teams demonstrated the highest performance
across the evaluation criteria while Tier 2 teams demonstrated strong performance across the evaluation criteria.
The winners are listed below in alphabetical order by Team Lead within each Tier.
Congratulations to the following Stage 1B Primary Track winners!
| Team Lead Entity |
Summary |
| Derq USA, Inc. |
-
Utilizes an approach that fuses varied perception sensors and learns from historical data
to build real-time situational awareness to monitor and analyze road user behavior.
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Developed real-time cooperative perception technology based on computer vision, machine learning,
and sensor fusion with applications in traffic control, connected vehicles and safety analytics
including illegal road-user movement detection, real-time near-miss (conflict) detection
and real time crash detection for vehicles and vulnerable road users.
-
Approach includes a process for sensor calibration, including the alignment of camera and LiDAR data,
and synchronization of timestamps for data integration.
-
Perception algorithms employed to detect and classify road users in a variety of sensor feeds,
which are tracked and then fused in a common frame of reference.
-
Path prediction models are applied to anticipate the future movements of road users, feeding
into conflict detection algorithms to identify potential collision scenarios.
|
| University of California, Los Angeles (UCLA) Mobility Lab |
-
InfraShield system uses sensor fusion and path prediction technologies which leverage multimodal sensor data,
including LiDAR, red, green, and blue wavelengths (RGB) cameras, and radar, to detect, classify,
and track vulnerable road users and vehicles under challenging conditions.
-
Utilizes a late fusion approach to combine sensor data for object detection, classification,
and tracking, addressing calibration issues and sensor limitations.
-
For path prediction, InfraShield employs machine learning models to forecast future movements
of road users, utilizing high-definition maps and historical object trajectory data, accounting
for diverse paths of vehicles and vulnerable road users, ensuring robust predictions despite noise data,
and can be used to identify conflict points using time-to-collision calculations.
|
| University of Hawaii |
-
Relies on sensor fusion across multiple modalities including LiDAR, RGB cameras, thermal cameras
and signal data providing highly accurate 3D localization, open vocabulary detection
for even potentially unknown test-time classes and multi-mode probabilistic path prediction,
which are combined for conflict prediction.
-
Approach in optimal utilization and fusion of sensors, allows real-time inference on cheaper devices,
minimizes data curation costs and ensures good generalization across conditions, which are crucial
to ensure scalability to intersections all over the nation.
|
| University of Michigan |
-
Team includes Mcity of the University of Michigan, General Motors Global R&D, Ouster, and Texas A&M University.
-
SAFETI real-time algorithms are designed to work with DOT-supplied sensor data, focusing on identifying
and predicting the movement of vehicles and vulnerable road users at intersections.
-
The approach integrates 2D detection from images and 3D detection from LiDAR data, followed by sensor fusion
and trajectory prediction, with a conflict detection module that evaluates potential collisions
between agents in real-time.
|
| Team Lead Entity |
Summary |
| Florida A&M University (FAMU) and Florida State University (FSU) |
-
Predictive Intersection Safety System's (PREDISS's) goal is to leverage machine learning,
controls, optimization, connected and autonomous vehicles technologies to improve the safety
of vulnerable road users at signalized intersections.
-
Approach fuses low-cost sensors' data to detect (differentiate and classify), localize,
track, and predict the trajectories of vehicles and vulnerable road users.
-
Strikes a balance between compute power and practicality, factoring in the long-term goal
of retrofitting such a system in intersections across the United States. Designed system
in collaboration with the Tallahassee Advanced Traffic Management System (TATMS)
to be deployable on existing infrastructure, including testing on live feeds.
-
Key design choices include: 1) Modular architecture; 2) User-friendly calibration;
3) Python-based implementation; 4) Efficient algorithms; 5) Adaptive fusion techniques
|
| Miovision (Global Traffic Technologies) |
-
Team includes Miovision USA, Carnegie Mellon University, Amazon Web Services, and Telus.
-
Devised a perception, path prediction, and conflict prediction framework centered
around RGB camera and LiDAR sensors.
-
Emphasizing decision-level sensor fusion, approach amalgamates independent detections
from multiple strategically positioned cameras with granular 3D spatial details
from LiDAR data, fostering enhanced detection and localization.
-
Perception module encompasses components such as refined YOLO-based object detection
and classification in 2D and LiDAR-based object detection in 3D, multi-camera object
tracking based on DeepSORT, and advanced LiDAR-based 3D object localization.
-
Subsequent path prediction, bolstered by an expanded dataset and the AutoBots-Joint model,
predicts complex scenarios for each road user into the future at intersections,
using bird's-eye-view projections enriched by PCA-based ground plane estimations.
-
At the end, conflict prediction framework integrates time-based Surrogate Safety Measure
of Time to Collision to capture complex interactions to anticipate potential
collision scenarios, complemented by probabilistic filtering to reduce false positives.
|
| Ohio State University |
-
Approach leverages a late fusion strategy that integrates data from LiDAR, RGB cameras,
and infrared sensors.
-
Utilizes a Euler-Region Proposal Network (E-RPN) to process Bird's Eye View (BEV) projections
of LiDAR point cloud data.
-
Concurrently, a YOLOv10 network is employed for 2D object detection, and a ByteTrack2 tracker
is used to track 2D bounding boxes over time. YOLO is applied independently to both RGB
and infrared images to maximize detection accuracy.
-
By analyzing the velocity states of the tracked objects, it predicts their future trajectories
over a specified time horizon, assuming constant velocity and performing linear extrapolation.
-
Potential collisions are identified by examining the predicted trajectories on the x, y plane.
|
| Orion Robotics Labs |
-
Orion Robotics Labs is a small woman-owned business in rural Colorado. Orion Robotics Labs
has expertise in machine learning, edge compute, sensing technologies and robotics.
-
Developed a solution for detection, localization, classification and path/conflict detection
to increase intersection safety by combining lightweight algorithms, fine-tuned calibrations
and fast processing.
|
| University of California, Riverside |
-
Approach aims to develop an Intersection Safety System (ISS) using roadside sensor-based data,
vehicle-to-everything (V2X) communications, and artificial intelligence (AI)
to continuously monitor traffic, predict traffic states (including trajectories)
and potential conflicts, as well as to enhance vulnerable road user safety
at signalized intersections, with Stage 1B focus on roadside perception and collision prediction.
-
The approach developed centers around the following modules: 1) Data Processing; 2) Sensor Fusion;
3) Multi-Object Tracking; 4) Path Prediction; 5) Collision Prediction.
-
Integrates computer vision technologies and other machine learning techniques
for road users' detection (also including sub-classification and localization), tracking,
path prediction, and conflict prediction at signalized intersections.
|
| University of Washington |
-
Developed a Cooperative Perception System (CPS) to generate a comprehensive understanding
of intersection dynamics.
-
System integrates multiple sensors, including eight visual cameras, five thermal cameras,
and two 3D LiDARs, enabling 3D object detection, classification, and path and conflict prediction.
-
Architecture of CPS is structured into three primary modules: Object Detection
and Classification, 2D-3D Camera Calibration, and Tracking and Prediction.
-
Object Detection and Classification Module acts as the foundation of the CPS
and processes incoming data from both visual and thermal cameras to detect
and classify road users in various lighting conditions.
-
2D-3D Camera Calibration Module converts 2D detection results into 3D object representations
using a multi-sensor re-identification process that merges data from cameras and 3D LiDAR sensors.
-
Tracking and Prediction Module utilizes the DeepSORT algorithm to track the 3D detections,
capturing crucial movement data such as trajectories, speeds, and orientations.
This information feeds into a Seq2Seq prediction model, which processes the sequences
of past object states to forecast future movements. The model predicts potential paths
and identifies possible conflicts by calculating the time-to-collision (TTC),
thus assessing the likelihood of hazardous interactions.
|
Improving the safety of pedestrians, bicyclists, and other vulnerable road users is of critical importance
to achieving the U.S. DOT's vision of zero roadway deaths and serious injuries. The Intersection Safety Challenge
supports these Departmental priorities, aligns with the
U.S. DOT's National Roadway Safety Strategy (NRSS),
and aims to set the stage for the future deployment of roadway intersection safety systems nationwide.
Given the overwhelming interest in Stage 1B, U.S. DOT is exploring ways to engage all interested parties
in future stages of the Intersection Safety Challenge. For updates on opportunities to participate
in the Intersection Safety Challenge in the coming year, please visit https://its.dot.gov/isc/.
