Pylot: A Modular Platform for Exploring Latency-Accuracy Tradeoffs in Autonomous Vehicles

Gog, Ionel; Kalra, Sukrit; Schafhalter, Peter; Wright, Matthew A.; Stoica, Ion

doi:10.1109/icra48506.2021.9561747

Cited by 38 publications

(12 citation statements)

References 29 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…STI can also be directly integrated into the planner [28], alleviating the need for the mitigation controller. And finally, STI can be integrated with systems like PyLot [29] and others [30] for deadline-aware scheduling tasks.…”

Section: Discussionmentioning

confidence: 99%

Watch Out for the Safety-Threatening Actors: Proactively Mitigating Safety Hazards

Cui¹,

Kalbarczyk²,

Iyer³

2022

Preprint

View full text Add to dashboard Cite

Despite the successful demonstration of autonomous vehicles (AVs), such as selfdriving cars, ensuring AV safety remains a challenging task. Although some actors influence an AV's driving decisions more than others, current approaches pay equal attention to each actor on the road. An actor's influence on the AV's decision can be characterized in terms of its ability to decrease the number of safe navigational choices for the AV. In this work, we propose a safety threat indicator (STI) using counterfactual reasoning to estimate the importance of each actor on the road with respect to its influence on the AV's safety. We use this indicator to (i) characterize the existing real-world datasets to identify rare hazardous scenarios as well as the poor performance of existing controllers in such scenarios; and (ii) design an RL based safety mitigation controller to proactively mitigate the safety hazards those actors pose to the AV. Our approach reduces the accident rate for the state-of-the-art AV agent(s) in rare hazardous scenarios by more than 70%. * Equal contribution.Preprint. Under review.

show abstract

Section: Discussionmentioning

confidence: 99%

Watch Out for the Safety-Threatening Actors: Proactively Mitigating Safety Hazards

Cui¹,

Kalbarczyk²,

Iyer³

2022

Preprint

View full text Add to dashboard Cite

show abstract

“…The main component of the simulation is the simulator itself. In this study, we chose to use CARLA [14] due to its flexibility, realism (in rendering visuals and physics), and the availability of open-source support for integrating with ADSs such as OpenPilot (Level 2/3) [23] and Pylot (Level 4 ADS) [24]. The simulation involved two steps: (1) Simulating the environment, and (2) Simulating the Automated Driving Systems (ADSs).…”

Section: A Av Simulationmentioning

confidence: 99%

“…The parameters and their corresponding values are shown in Table 1 (details in Supplementary material Section 1). 2) Pylot [24] from the Erdos Project (Level 4 ADS).…”

Section: ) Simulate the Environmentmentioning

confidence: 99%

Deriving Environmental Risk Profiles for Autonomous Vehicles From Simulated Trips

Anih

Kolekar²,

Dargahi

et al. 2023

IEEE Access

View full text Add to dashboard Cite

The commercial adoption of Autonomous Vehicles (AVs) and the positive impact they are expected to have on traffic safety depends on appropriate insurance products due to the high potential losses. A significant proportion of these losses are expected to occur from the out-of-distribution risks which arise from situations outside the AV's training experience. Traditional vehicle insurance products (for humandriven vehicles) rely on large data sets of drivers' background and historical incidents. However, the lack of such datasets for AVs makes it imperative to exploit the ability to deploy AVs in simulated environments. In this paper, the data collected by deploying Autonomous Driving Systems (ADSs) in simulated environments is used to develop models to answer two questions: (1) how risky a road section is for an AV to drive? and (2) how does the risk profile vary with different (SAE levels) of ADSs? A simulation pipeline was built on the CARLA (Car Learning to Act): an open-source simulator for autonomous driving research). The environment was specified using parameters such as weather, lighting, traffic density, traffic flow, no. of lanes, etc. A metric -risk factor was defined as a combination of harsh accelerations/braking, inverse Time to Collision, and inverse Time Headway to capture the crashes and near crashes. To assess the difference between ADSs, two ADSs: OpenPilot (Level 2/3) and Pylot (Level 4) were implemented in the simulator. The results (from data and model predictions) show that the trends in the relation between the environment features and risk factor for an AV are similar to those observed for human drivers (e.g., risk increases with traffic flow). The models also showed that junctions were a risk hot-spot for both ADSs. The feature importance of the model revealed that the Level 2/3 ADS is more sensitive to no. of lanes and the Level 4 ADS is sensitive to traffic flow. Such differences in feature importance provide valuable insights into the risk characteristics of different ADSs. In the future, this base model will be extended to include other features (other than the environment), e.g., take over requests, and also address the deficiencies of the current simulation data in terms of insensitivity to weather and lighting.

show abstract

“…Autonomous driving is conducted via several modules [1], [2], namely localization [3], perception [4], planning [5], and control. However, the system performance is constrained by the manually selected intermediate criteria, e.g., localization and lane detection error.…”

Section: Introductionmentioning

confidence: 99%

MMFN: Multi-Modal-Fusion-Net for End-to-End Driving

Zhang¹,

Tang²,

Geng³

et al. 2022

Preprint

View full text Add to dashboard Cite

Inspired by the fact that humans use diverse sensory organs to perceive the world, sensors with different modalities are deployed in end-to-end driving to obtain the global context of the 3D scene. In previous works, camera and LiDAR inputs are fused through transformers for better driving performance. These inputs are normally further interpreted as high-level map information to assist navigation tasks. Nevertheless, extracting useful information from the complex map input is challenging, for redundant information may mislead the agent and negatively affect driving performance. We propose a novel approach to efficiently extract features from vectorized High-Definition (HD) maps and utilize them in the end-to-end driving tasks. In addition, we design a new expert to further enhance the model performance by considering multi-road rules. Experimental results prove that both of the proposed improvements enable our agent to achieve superior performance compared with other methods. The source code is released as an open-source package.• We use a multi-path consideration rule-based expert to

show abstract

Pylot: A Modular Platform for Exploring Latency-Accuracy Tradeoffs in Autonomous Vehicles

Cited by 38 publications

References 29 publications

Watch Out for the Safety-Threatening Actors: Proactively Mitigating Safety Hazards

Watch Out for the Safety-Threatening Actors: Proactively Mitigating Safety Hazards

Deriving Environmental Risk Profiles for Autonomous Vehicles From Simulated Trips

MMFN: Multi-Modal-Fusion-Net for End-to-End Driving

Contact Info

Product

Resources

About