Route-Time Conflict Maps for Cooperative End-to-End Autonomous Driving
Overview
This research presents When2Go, a cooperative end-to-end driving system designed to share intent rather than just perception data, enabling vehicles to resolve conflicts under SAE J3016 Class B or higher automation. By broadcasting LiDAR detections with forecast motion and intended trajectories, the system constructs route-time conflict maps where an order gate network decides precedence based on right-of-way rules. Trained by imitating a rule-based expert from V2Xverse with relabeling for unobservable stops, When2Go achieves state-of-the-art performance on three closed-loop CARLA benchmarks: 97.8 and 90.7 driving scores on InterDrive (without and with traffic), 70.0 on the public V2Xverse protocol, and 88.2 on MDrive, with 95.0% of vehicles remaining collision-free during evaluation.
Generated from attributed reports · Updated 5 hours ago
Event evidence and corrections
0 attributed source owners. Ownership does not establish independent confirmation. Quantities are reported separately and are never added together.
Reported quantity · driving score: 97.8 other · Basis not reported · Differing source assertions
“s not learn to wait without an observable cause. For our runs only, the benchmarks' PID controller steers by When2Go's waypoints instead of the route, and its forced push of long-waiting vehicles is off. With one model, When2Go sets the state of the art on three closed-loop CARLA benchmarks: 97.8 and 90.7 driving score on InterDrive without and with traffic (best published 90.1 and 78.9), 70.0 on the public V2Xverse”
Exact source · revision 1Source owner not reported
Reported quantity · driving score: 90.7 other · Basis not reported · Differing source assertions
“s not learn to wait without an observable cause. For our runs only, the benchmarks' PID controller steers by When2Go's waypoints instead of the route, and its forced push of long-waiting vehicles is off. With one model, When2Go sets the state of the art on three closed-loop CARLA benchmarks: 97.8 and 90.7 driving score on InterDrive without and with traffic (best published 90.1 and 78.9), 70.0 on the public V2Xverse”
Exact source · revision 1Source owner not reported
Reported quantity · driving score: 90.1 other · Basis not reported · Differing source assertions
“s not learn to wait without an observable cause. For our runs only, the benchmarks' PID controller steers by When2Go's waypoints instead of the route, and its forced push of long-waiting vehicles is off. With one model, When2Go sets the state of the art on three closed-loop CARLA benchmarks: 97.8 and 90.7 driving score on InterDrive without and with traffic (best published 90.1 and 78.9), 70.0 on the public V2Xverse”
Exact source · revision 1Source owner not reported
Reported quantity · driving score: 78.9 other · Basis not reported · Differing source assertions
“s not learn to wait without an observable cause. For our runs only, the benchmarks' PID controller steers by When2Go's waypoints instead of the route, and its forced push of long-waiting vehicles is off. With one model, When2Go sets the state of the art on three closed-loop CARLA benchmarks: 97.8 and 90.7 driving score on InterDrive without and with traffic (best published 90.1 and 78.9), 70.0 on the public V2Xverse”
Exact source · revision 1Source owner not reported
Reported quantity · driving score: 70 other · Basis not reported · Differing source assertions
“s not learn to wait without an observable cause. For our runs only, the benchmarks' PID controller steers by When2Go's waypoints instead of the route, and its forced push of long-waiting vehicles is off. With one model, When2Go sets the state of the art on three closed-loop CARLA benchmarks: 97.8 and 90.7 driving score on InterDrive without and with traffic (best published 90.1 and 78.9), 70.0 on the public V2Xverse”
Exact source · revision 1Source owner not reported
Report timeline
Follow attributed reports and material updates.
- arXiv Robotics — research abstractsSignalRoute-Time Conflict Maps for Cooperative End-to-End Autonomous Driving
This research presents When2Go, a cooperative end-to-end driving system designed to share intent rather than just perception data, enabling vehicles to resolve conflicts under SAE J3016 Class B or higher automation. By broadcasting LiDAR detections with forecast motion and intended trajectories, the system constructs route-time conflict maps where an order gate network decides precedence based on right-of-way rules. Trained by imitating a rule-based expert from V2Xverse with relabeling for unobservable stops, When2Go achieves state-of-the-art performance on three closed-loop CARLA benchmarks: 97.8 and 90.7 driving scores on InterDrive (without and with traffic), 70.0 on the public V2Xverse protocol, and 88.2 on MDrive, with 95.0% of vehicles remaining collision-free during evaluation.
Event coverage history
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