INTENT: An LSTM Framework for Vehicle Intention Prediction in Intersection Scenarios with Comprehensive Ablation Analysis
arXiv:2607.08316v1 Announce Type: cross Abstract: Vehicle intention prediction is a pivotal aspect in the agility and safety of autonomous vehicles in all driving scenarios; if genuine enhancement of autonomous vehicles are required, we need to make them adopt human interpretation of driver's intention especially in cases that require a lot of human interaction as well as complex driving behaviors like the ones at intersections, roundabouts and emergency cases such as sudden stops where vehicle intention prediction helps in taking the correct evasive action within a real time period where ever
The continuous advancements in AI and specifically in Long Short-Term Memory (LSTM) frameworks are enabling more sophisticated solutions for autonomous driving challenges at this juncture.
Improved vehicle intention prediction, especially in complex scenarios like intersections, is critical for increasing the safety and efficacy of autonomous vehicles, directly impacting their commercial viability and public acceptance.
Autonomous vehicles gain a more robust capability to anticipate human driver behavior, leading to safer and more fluid navigation in real-world, interactive driving situations.
- · Autonomous vehicle manufacturers
- · AI developers specializing in perception
- · Logistics and transportation companies
- · Insurance companies (potentially lower accident rates)
- · Traditional human-driven taxi services
Further acceleration of autonomous vehicle development and deployment across various sectors.
Potential for new regulatory frameworks and infrastructure adaptations to support increased AV presence on roads.
Long-term shifts in urban planning and personal mobility habits as autonomous transportation becomes more prevalent and reliable.
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Read at arXiv cs.LG